{
    "paper_id": "P16-1025",
    "header": {
        "generated_with": "S2ORC 1.0.0",
        "date_generated": "2023-01-19T08:57:42.490656Z"
    },
    "title": "Liberal Event Extraction and Event Schema Induction",
    "authors": [
        {
            "first": "Lifu",
            "middle": [],
            "last": "Huang",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Rensselaer Polytechnic Institute",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Taylor",
            "middle": [],
            "last": "Cassidy",
            "suffix": "",
            "affiliation": {
                "laboratory": "US Army Research Lab",
                "institution": "",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Xiaocheng",
            "middle": [],
            "last": "Feng",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Harbin Institute of Technology",
                "location": {}
            },
            "email": "xcfeng@ir.hit.edu.cn"
        },
        {
            "first": "Heng",
            "middle": [],
            "last": "Ji",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Rensselaer Polytechnic Institute",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Clare",
            "middle": [
                "R"
            ],
            "last": "Voss",
            "suffix": "",
            "affiliation": {
                "laboratory": "US Army Research Lab",
                "institution": "",
                "location": {}
            },
            "email": "clare.r.voss.civ@rpi.edu"
        },
        {
            "first": "Jiawei",
            "middle": [],
            "last": "Han",
            "suffix": "",
            "affiliation": {},
            "email": ""
        },
        {
            "first": "Avirup",
            "middle": [],
            "last": "Sil",
            "suffix": "",
            "affiliation": {},
            "email": ""
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    "year": "",
    "venue": null,
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    "abstract": "We propose a brand new \"Liberal\" Event Extraction paradigm to extract events and discover event schemas from any input corpus simultaneously. We incorporate symbolic (e.g., Abstract Meaning Representation) and distributional semantics to detect and represent event structures and adopt a joint typing framework to simultaneously extract event types and argument roles and discover an event schema. Experiments on general and specific domains demonstrate that this framework can construct high-quality schemas with many event and argument role types, covering a high proportion of event types and argument roles in manually defined schemas. We show that extraction performance using discovered schemas is comparable to supervised models trained from a large amount of data labeled according to predefined event types. The extraction quality of new event types is also promising.",
    "pdf_parse": {
        "paper_id": "P16-1025",
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        "abstract": [
            {
                "text": "We propose a brand new \"Liberal\" Event Extraction paradigm to extract events and discover event schemas from any input corpus simultaneously. We incorporate symbolic (e.g., Abstract Meaning Representation) and distributional semantics to detect and represent event structures and adopt a joint typing framework to simultaneously extract event types and argument roles and discover an event schema. Experiments on general and specific domains demonstrate that this framework can construct high-quality schemas with many event and argument role types, covering a high proportion of event types and argument roles in manually defined schemas. We show that extraction performance using discovered schemas is comparable to supervised models trained from a large amount of data labeled according to predefined event types. The extraction quality of new event types is also promising.",
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                "section": "Abstract",
                "sec_num": null
            }
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        "body_text": [
            {
                "text": "Event extraction aims at identifying and typing trigger words and participants (arguments). It remains a challenging and costly task. The first question is what to extract? The TIP-STER (Onyshkevych et al., 1993) , MUC (Grishman and Sundheim, 1996) , CoNLL (Tjong et al., 2003; Pradhan et al., 2011) , ACE 1 and TAC-KBP (Ji and Grishman, 2011) programs found that it was feasible to manually define an event schema based on the needs of potential users. An ACE event schema example is shown in Figure 1 . This process is very expensive because consumers and 1 http://www.itl.nist.gov/iad/mig/tests/ace/ expert linguists need to examine a lot of data before specifying the types of events and argument roles and writing detailed annotation guidelines for each type in the schema. Manually-defined event schemas often provide low coverage and fail to generalize to new domains. For example, none of the aforementioned programs include \"donation\" and \"evacuation\" in their schema in spite of their potential relevance to users.",
                "cite_spans": [
                    {
                        "start": 186,
                        "end": 212,
                        "text": "(Onyshkevych et al., 1993)",
                        "ref_id": "BIBREF39"
                    },
                    {
                        "start": 219,
                        "end": 248,
                        "text": "(Grishman and Sundheim, 1996)",
                        "ref_id": "BIBREF15"
                    },
                    {
                        "start": 257,
                        "end": 277,
                        "text": "(Tjong et al., 2003;",
                        "ref_id": "BIBREF51"
                    },
                    {
                        "start": 278,
                        "end": 299,
                        "text": "Pradhan et al., 2011)",
                        "ref_id": "BIBREF43"
                    },
                    {
                        "start": 320,
                        "end": 343,
                        "text": "(Ji and Grishman, 2011)",
                        "ref_id": "BIBREF22"
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                    {
                        "start": 494,
                        "end": 502,
                        "text": "Figure 1",
                        "ref_id": "FIGREF0"
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                ],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "In this paper we propose Liberal Event Extraction, a new paradigm to take humans out of the loop and enable systems to extract events in a more liberal fashion. It automatically discovers a complete event schema, customized for a specific input corpus. Figure 1 compares the ACE event extraction paradigm and our proposed Liberal event extraction paradigm.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 253,
                        "end": 261,
                        "text": "Figure 1",
                        "ref_id": "FIGREF0"
                    }
                ],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "We use the following examples to explain and motivate our approach, where event triggers are in bold and arguments are in italics and underlined: E1. Two Soldiers were killed and one injured in the close-quarters fighting in Kut.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "E2. Bill Bennet's glam gambling loss changed my opinion.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "E3. Gen. Vincent Brooks announced the capture of Barzan Ibrahim Hasan al-Tikriti, telling reporters he was an adviser to Saddam.",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
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            {
                "text": "E4. This was the Italian ship that was captured by Palestinian terrorists back in 1985.",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
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            {
                "text": "E5. Ayman Sabawi Ibrahim was arrested in Tikrit and was sentenced to life in prison.",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
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                "text": "We seek to cluster the event triggers and event arguments so that each cluster represents a type. We rely on distributional similarity for our clustering distance metric. The distributional hypothesis (Harris, 1954) states that words often occurring in similar contexts tend to have similar meanings. We formulate the following distributional Guidelines Documents",
                "cite_spans": [
                    {
                        "start": 201,
                        "end": 215,
                        "text": "(Harris, 1954)",
                        "ref_id": "BIBREF16"
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                "section": "Introduction",
                "sec_num": "1"
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            {
                "text": "Sen 1: The Indian army stated that 4 Islamic militants were killed in 2 separate gun battles 20021228.",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "Sen 2: The embassy stated the British government is opposed to the death penalty in all circumstances.",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
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            {
                "text": "Event : killed, Type: Die, Arguments: 4 Islamic militants (Victim)",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "Linguistic Resource Documents",
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                "section": "Null",
                "sec_num": null
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            {
                "text": "Sen 1: The Indian army stated that 4 Islamic militants were killed in 2 separate gun battles 20021228.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Null",
                "sec_num": null
            },
            {
                "text": "Sen 2: The embassy stated the British government is opposed to the death penalty in all circumstances. hypotheses specifically for event extraction, and develop our approach accordingly.",
                "cite_spans": [],
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                "section": "Null",
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                "text": "Hypothesis 1: Event triggers that occur in similar contexts and share the same sense tend to have similar types.",
                "cite_spans": [],
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                "section": "Null",
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                "text": "Following the distributional hypothesis, when we simply learn general word embeddings from a large corpus for each word, we obtain similar words like those shown in Table 1 . We can see similar words, such as those centered around \"injure\" and \"fight\", are converging to similar types. However, for words with multiple senses such as \"fire\" (shooting or employment termination), similar words may indicate multiple event types. Thus, we propose to apply Word Sense Disambiguation (WSD) and learn a distinct embedding for each sense (Section 2.3). Hypothesis 2: Beyond the lexical semantics of a particular event trigger, its type is also dependent on its arguments and their roles, as well as other words contextually connected to the trigger.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 165,
                        "end": 172,
                        "text": "Table 1",
                        "ref_id": "TABREF3"
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                ],
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                "section": "Null",
                "sec_num": null
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            {
                "text": "For example, in E4, the fact that the patient role is a vehicle (\"Italian ship\"), and not a person (as in E3 and E5), suggests that the event trigger \"captured\" has type \"Transfer-Ownership\" as opposed to \"Arrest\". In E2, we know the \"loss\" event occurs in a gambling scenario, so we can determine its type as loss of money, not loss of life.",
                "cite_spans": [],
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                "section": "Null",
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                "text": "We therefore propose to enrich each trigger's representation by incorporating the distributional representations of various words in the trigger's context. Not all context words are relevant to event trigger type prediction, while those that are vary in their predictive value. We propose to use semantic relations, derived from a meaning representation for the text, to carefully select arguments and other words in an event trigger's context. These words are then incorporated into a \"global\" event structure for a trigger mention. We rely on semantic relations to (1) specify how the distributional semantics of relevant context words contribute to the overall event structure representation; (2) determine the order in which distributional semantics of relevant context words are incorporated into the event structure (Section 2.4). Liberal Event Extraction. Given a set of input documents, we first extract semantic relations, apply WSD and learn word sense embeddings. Next, we identify candidate triggers and arguments. For each event trigger, we apply a series of compositional functions to generate that trigger's event structure representation. Each function is specific to a semantic relation, and operates over vectors in the embedding space. Argument representations are generated as a by-product.",
                "cite_spans": [],
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                "text": "Trigger and argument representations are then passed to a joint constraint clustering framework. Finally, we name each cluster of triggers, and name each trigger's arguments using mappings between the meaning representation and semantic role descriptions in FrameNet, VerbNet (Kipper et al., 2008) and Propbank (Palmer et al., 2005) .",
                "cite_spans": [
                    {
                        "start": 276,
                        "end": 297,
                        "text": "(Kipper et al., 2008)",
                        "ref_id": "BIBREF24"
                    },
                    {
                        "start": 311,
                        "end": 332,
                        "text": "(Palmer et al., 2005)",
                        "ref_id": "BIBREF40"
                    }
                ],
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                "section": "Overview",
                "sec_num": "2.1"
            },
            {
                "text": "We compare settings in which semantic relations connecting triggers to context words are derived from three meaning representations: Abstract Meaning Representation (AMR) (Banarescu et al., 2013), Stanford Typed Dependencies (Marie-Catherine et al., 2006) , and FrameNet (Baker and Sato, 2003) . We derive semantic relations automatically for these three representations using CAMR (Wang et al., 2015a ), Stanford's dependency parser (Manning, 2003) , and SEMAFOR (Das et al., 2014) , respectively.",
                "cite_spans": [
                    {
                        "start": 225,
                        "end": 255,
                        "text": "(Marie-Catherine et al., 2006)",
                        "ref_id": "BIBREF31"
                    },
                    {
                        "start": 271,
                        "end": 293,
                        "text": "(Baker and Sato, 2003)",
                        "ref_id": "BIBREF0"
                    },
                    {
                        "start": 382,
                        "end": 401,
                        "text": "(Wang et al., 2015a",
                        "ref_id": "BIBREF53"
                    },
                    {
                        "start": 434,
                        "end": 449,
                        "text": "(Manning, 2003)",
                        "ref_id": "BIBREF30"
                    },
                    {
                        "start": 464,
                        "end": 482,
                        "text": "(Das et al., 2014)",
                        "ref_id": "BIBREF11"
                    }
                ],
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                "section": "Overview",
                "sec_num": "2.1"
            },
            {
                "text": "Given a sentence, we consider all noun and verb concepts that are assigned an OntoNotes (Hovy et al., 2006) sense by WSD as candidate event triggers. Any remaining concepts that match both a verbal and a nominal lexical unit in the FrameNet corpus are considered candidate event triggers as well. This mainly helps to identify more nominal triggers like \"pickpocket\" and \"sin\". 2 For each candidate event trigger, we consider as candidate arguments all concepts for which one of a manually-selected set of semantic relations holds between it and the event trigger. For the setting in which AMR serves as our meaning representation, we selected a subset of all AMR relations that specify event arguments, as shown in Table 2. Note that some AMR relations generally do not specify event arguments, e.g. \"mode\", which can indicate sentence illocutionary force, or \"snt\" which is used to combine multiple sentences into one AMR graph. 3 When FrameNet is the meaning representation we allow all frame relations to identify arguments. For dependencies, we manually mapped dependency relations to AMR relations and use Table 2 .",
                "cite_spans": [
                    {
                        "start": 88,
                        "end": 107,
                        "text": "(Hovy et al., 2006)",
                        "ref_id": "BIBREF20"
                    }
                ],
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                    {
                        "start": 1112,
                        "end": 1119,
                        "text": "Table 2",
                        "ref_id": "TABREF4"
                    }
                ],
                "eq_spans": [],
                "section": "Candidate Trigger and Argument Identification",
                "sec_num": "2.2"
            },
            {
                "text": "Relations Core roles ARG0, ARG1, ARG2, ARG3, ARG4 Non-core roles mod, location, poss, manner, topic, medium, instrument, duration, prep-X Temporal year, duration, decade, weekday, time Spatial destination, path, location In E1, for example, \"killed\", \"injured\" and \"fighting\" are identified as candidate triggers, and three concept sets are identified as candidate arguments using AMR relations: \"{Two Soldiers, very large missile}\", \"{one, Kut}\" and \"{Two Soldiers, Kut}\", as shown in Figure 3 .",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 486,
                        "end": 494,
                        "text": "Figure 3",
                        "ref_id": "FIGREF3"
                    }
                ],
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                "section": "Categories",
                "sec_num": null
            },
            {
                "text": "Based on Hypothesis 1, we learn sense-based embeddings from a large data set, using the Continuous Skip-gram model (Mikolov et al., 2013) . Specifically, we first apply WSD to link each word to its sense in WordNet using a state-of-the-art tool (Zhong and Ng, 2010) , and map WordNet sense output to OntoNotes senses. 4 We map each trigger candidate to its OntoNotes sense and learn a distinct embedding for each sense. We use general lexical embeddings for arguments.",
                "cite_spans": [
                    {
                        "start": 115,
                        "end": 137,
                        "text": "(Mikolov et al., 2013)",
                        "ref_id": "BIBREF33"
                    },
                    {
                        "start": 245,
                        "end": 265,
                        "text": "(Zhong and Ng, 2010)",
                        "ref_id": "BIBREF58"
                    },
                    {
                        "start": 318,
                        "end": 319,
                        "text": "4",
                        "ref_id": null
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Trigger Sense and Argument Representation",
                "sec_num": "2.3"
            },
            {
                "text": "Based on Hypothesis 2, we aim to exploit linguistic knowledge to incorporate inter-dependencies between event and argument role types into our event structure representation. Many meaning representations could provide such information to some degree. We illustrate our method for building event structures using semantic relations from meaning representations using AMR. In Section 3.4 we compare results using Stanford Typed Dependencies and FrameNet in place of AMR. Let's take E2 as an example. Based on AMR annotation and Table 2 , we extract semantically re-E1: Two Soldiers were killed by a very large missile and one injured in the close-quarters fighting in Kut. an(Agent), Austrialia(Destination),heroin(Theme) identify the 2 convicts hanged in Zahedan but stated d guilty of transporting 5.25 kilograms of heroin. ting, Arguments: they(Agent), heroin (Theme) of the facility started in 790000, but stopped after lapse when Tajikistan slid into a 5 year civil war onomy. Event:construction, uments: facility(Product), 790000(Time) era military facility was fou-nded in 570000 and all information gathered from Russia's military spy Event: founded, s: Soviet-era facility(Product), 570000(Time) Event Type: Build S2: A newspaper report on January 1, 2008 that Iran hanged two convicted drug traffickers in the south-eastern city of Zahedan. S1: Colombian Government was alarmed because uranium is the primary basis for generating weapons of mass destruction. Event:alarmed, Arguments:Columbian Government(Experiencer) lated words for the event trigger with sense \"lose-1\" and construct the event structure for the whole event, as shown in Figure 4 .",
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                    {
                        "start": 861,
                        "end": 868,
                        "text": "(Theme)",
                        "ref_id": null
                    }
                ],
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                        "start": 526,
                        "end": 533,
                        "text": "Table 2",
                        "ref_id": "TABREF4"
                    },
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                        "start": 1647,
                        "end": 1655,
                        "text": "Figure 4",
                        "ref_id": "FIGREF4"
                    }
                ],
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                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
            },
            {
                "text": "Z1=fmod(Wmod,Xga,Yl)=X T gaWmodYl+b Reconstruct: (X'ga,Y'l)=Z1W'mod+b' Z2=fmod(Wmod,Xgl,Z1) Z4=fposs(Wposs,Z3,Z2) Reconstruct: (Z'3,Z'2)=Z4W'poss+b' Z3=Avarage(VBill, VBennet) Z1 Z2 Z4 X'gamble Y'lose Z'3 Z'2 Reconstruct: (X'gl,Z'1)=Z2W'mod+b' X'glam Z'1",
                "cite_spans": [],
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                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
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                "text": "We design a Tensor based Recursive Auto-Encoder (TRAE) (Socher et al., 2011) framework to utilize a tensor based composition function for each of a subset of the AMR semantic relations and compose the event structure representation based on multiple functional applications. This subset was manually selected by the authors as the set of relations that link a trigger to concepts that help to determine its type. Similarly, we selected a subset of dependency and FrameNet relations using the same criteria for experiments using those meaning representations. Figure 4 shows an instance of a TRAE applied to an event structure to generate its representation. For each semantic relation type r, such as \":mod\", we define the output of a tensor product Z via the following vectorized notation:",
                "cite_spans": [
                    {
                        "start": 55,
                        "end": 76,
                        "text": "(Socher et al., 2011)",
                        "ref_id": "BIBREF48"
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                    {
                        "start": 559,
                        "end": 567,
                        "text": "Figure 4",
                        "ref_id": "FIGREF4"
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                ],
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                "section": "Event Structure Composition and Representation",
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            },
            {
                "text": "Z = f mod (X, Y, W [1:d] r , b) = [X; Y ] T W [1:d] r [X; Y ] + b",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
            },
            {
                "text": "where W mod \u2208 R 2d\u20222d\u2022d is a 3-order tensor, and X, Y \u2208 R d are two input word vectors. b \u2208 R d is the bias term. [X; Y ] denotes the concatenation of two vectors X and Y . Each slice of the tensor acts as a coefficient matrix for one entry Z i in Z:",
                "cite_spans": [],
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                "section": "Event Structure Composition and Representation",
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            },
            {
                "text": "Zi = f mod (X, Y, W [i] r , b) = [X; Y ] T W [i] r [X; Y ] + bi",
                "cite_spans": [],
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                "section": "Event Structure Composition and Representation",
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            },
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                "text": "We use the statistical mean to compose the words connected by \":op\" relations (e.g. \"Bill\" and \"Bennet\" in Figure 4) .",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 107,
                        "end": 116,
                        "text": "Figure 4)",
                        "ref_id": "FIGREF4"
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                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
            },
            {
                "text": "After composing the vectors of X and Y , we apply an element-wise sigmoid activation function to the composed vector and generate the hidden layer representations Z. One way to optimize Z is to try to reconstruct the vectors X and Y by generating X and Y from Z, and minimizing the reconstruction errors between the input",
                "cite_spans": [],
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                "section": "Event Structure Composition and Representation",
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            },
            {
                "text": "V I = [X, Y ] and out- put layers V O = [X , Y ].",
                "cite_spans": [],
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                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
            },
            {
                "text": "The error is computed based on Euclidean distance function:",
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                "eq_spans": [],
                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
            },
            {
                "text": "E(VI , VO) = 1 2 ||VI \u2212 VO|| 2",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
            },
            {
                "text": "For each pair of words X and Y , the reconstruction error back-propagates from its output layer to input layer through parameters \u0398 r = (W r , b r , W r , b r ). Let \u03b4 O be the residual error of the output layer, and \u03b4 H be the error of the hidden layer:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Event Structure Composition and Representation",
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            },
            {
                "text": "\u03b4O = \u2212(VI \u2212 VO) \u2022 f sigmoid (V O H ) \u03b4H = ( d k=1 \u03b4 k O \u2022 (W k r + (W k r ) T ) \u2022 V O H ) \u2022 f sigmoid (V I H )",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
            },
            {
                "text": "where V I H and V O H denote the input and output of the hidden layer, and V O H = Z. W k r is the k th slice of tensor W r .",
                "cite_spans": [],
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                "section": "Event Structure Composition and Representation",
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            },
            {
                "text": "To minimize the reconstruction errors, we utilize gradient descent to iteratively update parameters \u0398 r :",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
            },
            {
                "text": "\u2202E(\u0398r) \u2202W k r = \u03b4 k O \u2022 (V O H ) T \u2022 V O H \u2202E(\u0398r) \u2202b r = \u2212(VI \u2212 VO) \u2022 f sigmoid (V O H ) \u2202E(\u0398r) \u2202W k r = \u03b4 k H \u2022 (VI ) T \u2022 VI \u2202E(\u0398r) \u2202br = ( d k=1 \u03b4 k O \u2022 (W k r + (W k r ) T ) \u2022 V O H ) \u2022 f sigmoid (V I H )",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
            },
            {
                "text": "After computing the composition vector of Z 1 based on X and Y , for the next layer, it composes Z 1 and another new word vector such as X gl . For each type of relation r, we randomly sample 2,000 pairs to train optimized parameters \u0398 r . For each event structure tree, we iteratively repeat the same steps for each layer. For multiple arguments at each layer, we compose them in the order of their distance to the trigger: the closest argument is composed first.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Event Structure Composition and Representation",
                "sec_num": "2.4"
            },
            {
                "text": "Based on the representation vectors generated above, we compute the similarity between each pair of triggers and arguments, and cluster them into types. Recall that a trigger's arguments are identified as in section 2.2. We observe that, for two triggers t 1 and t 2 , if their arguments have the same type and role, then they are more likely to belong to the same type, and vice versa. Therefore we introduce a constraint function f , to enforce inter-dependent triggers and arguments to have coherent types:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "f (P1, P2) = log(1 + |L1 \u2229 L2| |L1 \u222a L2| )",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "where P1 and P2 are triggers. Elements of Li are pairs of the form (r, id(a)), where id(a) is the cluster ID for argument a that stands in relation r to Pi. For example, let P1 and P2 be triggers \"capture\" and \"arrested\" (c.f. Figure 5 ). If Barzan Ibrahim Hasan al-Tikriti and Ayman Sabawi Ibrahim share the same cluster ID, the pair (arg1, id(Barzan Ibrahim Hasan al-Tikriti)) will be a member of L 1 \u2229 L 2 . This argument overlap is evidence that \"capture\" and \"arrested\" have the same type. We define f where P i are arguments, and elements L i are defined analogously to above. Given a trigger set T and their corresponding argument set A, we compute the similarity between two triggers t 1 and t 2 and two arguments a 1 and a 2 by:",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 227,
                        "end": 235,
                        "text": "Figure 5",
                        "ref_id": "FIGREF6"
                    }
                ],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "sim(t1, t2) = \u03bb \u2022 simcos(E t 1 g , E t 2 g )+ (1 \u2212 \u03bb) \u2022 \u03a3r\u2208R t 1 \u2229R t 2 simcos(E t 1 r , E t 2 r ) |Rt 1 \u2229 Rt 2 | + f (t1, t2) sim(a1, a2) = simcos(E a 1 g , E a 2 g ) + f (a1, a2)",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "where E t g represents the trigger sense vector and E a g is the argument vector. R t is the AMR relation set in the event structure of t, and E t r denotes the vector resulting from the last application of the compositional function corresponding to the semantic relation r for trigger t. \u03bb is a regularization parameter that controls the trade-off between these two types of representations. In our experiment \u03bb = 0.6.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "We design a joint constraint clustering approach, which iteratively produces new clustering results based on the above constraints. To find a global optimum, which corresponds to an approximately optimal partition of the trigger set into K",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "clusters C T = {C T 1 , C T 2 , ..., C T K }",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": ", and a partition of the argument set into M clusters",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "C A = {C A 1 , C A 2 , ..., C A M },",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "we minimize the agreement across clusters and the disagreement within clusters:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "arg min K T ,K A ,\u03bb O = (D T inter + D T intra ) + (D A inter + D A intra ) D P inter = K i =j=1 u\u2208C P i ,v\u2208C P j sim(Pu, Pv) D P intra = K i=1 u,v\u2208C P i (1 \u2212 sim(Pu, Pv))",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "We incorporate the Spectral Clustering algorithm (Luxburg, 2007) into joint constraint clustering process to get the final optimized clustering results. The detailed algorithm is summarized in Algorithm 1.",
                "cite_spans": [
                    {
                        "start": 49,
                        "end": 64,
                        "text": "(Luxburg, 2007)",
                        "ref_id": "BIBREF28"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Joint Trigger and Argument Clustering",
                "sec_num": "2.5"
            },
            {
                "text": "For each trigger cluster, we utilize the trigger which is nearest to the centroid of the cluster as the event type name. For a given event trigger, we assign a role name to each of its arguments (identified as in section 2.2). This process depends on which meaning representation was used to select the arguments.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Event Type and Argument Role Naming",
                "sec_num": "2.6"
            },
            {
                "text": "For AMR, we first map the event trigger's OntoNotes sense to PropBank, VerbNet, and FrameNet. We assign each argument a role name as follows. We map AMR core roles (e.g. \":ARG0\", \"ARG1\") to FrameNet if possible, otherwise to VerbNet if possible, and finally to PropBank roles if a mapping to VerbNet is not available. 5 . Nearly 5% of AMR core roles can 5 OntoNotes 5.0 provides a mapping; https://catalog.ldc.upenn.edu/LDC2013T19",
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                    {
                        "start": 318,
                        "end": 319,
                        "text": "5",
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                        "start": 354,
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                        "text": "5",
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                "section": "Event Type and Argument Role Naming",
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            },
            {
                "text": "Algorithm 1 Joint Constraint Clustering Algorithm Input: Trigger set T , argument set A, their lexical embedding E T g , E A g , event structure representation E T R , and the minimal (K min T , K min A ) and maximal (K max T , K max A ) number of clusters for triggers and arguments; Output: The optimal clustering results: C T and C A ;",
                "cite_spans": [],
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                "section": "Event Type and Argument Role Naming",
                "sec_num": "2.6"
            },
            {
                "text": "\u2022 Omin = \u221e, C T = \u2205, C A = \u2205 \u2022 For KT = K min T to KT = K max T , KA = K min A to KA = K max A -Clustering with Spectral Clustering Algorithm: -C T curr = spectral(T, E T g , E T R , KT ) -C A curr = spectral(A, E A g , KA) -Ocurr = O(C T curr , C A curr ) -if Ocurr < Omin * Omin = Ocurr, C T = C T curr , C A = C A curr -while iterate time \u2264 10 * C T curr = spectral(T, E T g , E T R , KT , C A curr ) * C A curr = spectral(A, E A g , KA, C T curr ) * Ocurr = O(C T curr , C A curr ) * if Ocurr < Omin \u2022 Omin=Ocurr, C T = C T curr , C A = C A curr \u2022 return Omin, C T , C A ;",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Event Type and Argument Role Naming",
                "sec_num": "2.6"
            },
            {
                "text": "be mapped to FrameNet roles and 55% can be mapped to VerbNet roles, and the remaining can be mapped to PropBank. Table 3 shows some mapping examples. We map non-core roles from AMR to FrameNet, as shown in Table 4 . When Stanford Typed Dependencies are used for meaning representation we construct a manual mapping AMR relations and use the above procedure. When FrameNet is used for meaning representation we simply keep the FrameNet role name for argument role naming. compare the impact of perfect AMR and system generated AMR. To compare with state-of-the-art event extraction on Automatic Content Extraction (ACE2005) data, we follow the same evaluation setting in previous work (Ji and Grishman, 2008; Liao and Grishman, 2010; Hong et al., 2011) and use 40 newswire documents as our test set. Figure 6 shows some examples as part of the event schema discovered from the ERE data set. Each cluster denotes an event type, with a set of event mentions and sentences. Each event mention is also associated with some arguments and their roles. The event and argument role annotations for sample sentences may serve as an examplebased corpus-customized \"annotation guideline\" for event extraction. Table 5 compares the coverage of event schema discovered by our approach, using AMR as meaning representation, with the predefined ACE and ERE event schemas. Besides the types defined in ACE and ERE, this approach discovers many new event types such as Build and Threaten as displayed in Figure 6 . Our approach can also discover new argument roles for a given event type. For example, for Attack events, besides five types of existing arguments (Attacker, Target, Instrument, Time, and Place) defined in ACE, we also discover a new type of argument Purpose. For example, in \"The Dutch government, facing strong public anti-war pressure, said it would not commit fighting forces to the war against Iraq but added it supported the military campaign to disarm Saddam.\", \"disarm Saddam\" is identified as the Purpose for the Attack event triggered by \"campaign\". Note that while FrameNet specifies Purpose as an argument role for the Attack, such information specific to Attack is not part of AMR. ",
                "cite_spans": [
                    {
                        "start": 684,
                        "end": 707,
                        "text": "(Ji and Grishman, 2008;",
                        "ref_id": "BIBREF21"
                    },
                    {
                        "start": 708,
                        "end": 732,
                        "text": "Liao and Grishman, 2010;",
                        "ref_id": "BIBREF26"
                    },
                    {
                        "start": 733,
                        "end": 751,
                        "text": "Hong et al., 2011)",
                        "ref_id": "BIBREF19"
                    }
                ],
                "ref_spans": [
                    {
                        "start": 113,
                        "end": 120,
                        "text": "Table 3",
                        "ref_id": "TABREF7"
                    },
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                        "start": 206,
                        "end": 213,
                        "text": "Table 4",
                        "ref_id": "TABREF8"
                    },
                    {
                        "start": 799,
                        "end": 807,
                        "text": "Figure 6",
                        "ref_id": null
                    },
                    {
                        "start": 1198,
                        "end": 1205,
                        "text": "Table 5",
                        "ref_id": "TABREF10"
                    },
                    {
                        "start": 1486,
                        "end": 1494,
                        "text": "Figure 6",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Event Type and Argument Role Naming",
                "sec_num": "2.6"
            },
            {
                "text": "To evaluate the performance of the whole event schema, we randomly sample 100 sentences from ERE data set and ask two linguistic experts to fully annotate the events and arguments. As a starting point, annotators were given output from our Schema Discovery using gold standard AMR. For each sentence, they saw event triggers and corresponding arguments. Their job was to correct this output by marking incorrectly identified events and arguments, and adding missing events and arguments. The inter-annotator agreement is 83% for triggers and 79% for arguments. To evaluate trigger and argument identification, we automatically compare this gold standard with system output (see Table 6 ). To evaluate trigger and argument typing, annotators manually checked system output and assessed whether the type name was reasonable (see Table 6 ). Note that automatic comparison between system and gold standard output is not appropriate for typing; for a given cluster, there is no definitive \"best\" name.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 678,
                        "end": 685,
                        "text": "Table 6",
                        "ref_id": "TABREF12"
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                    {
                        "start": 827,
                        "end": 834,
                        "text": "Table 6",
                        "ref_id": "TABREF12"
                    }
                ],
                "eq_spans": [],
                "section": "Event Extraction for All Types",
                "sec_num": "3.3"
            },
            {
                "text": "We found that most event triggers not recovered by our system are multi-word expressions such as \"took office\" or adverbs such as \"previously\" and \"formerly\". For argument identification, our approach fails to identify some arguments that require world knowledge to extract. For example, in \"Anti-corruption judge Saul Pena stated Montesinos has admitted to the abuse of authority charge\", \"Saul Pena\" is not identified as a Adjudicator argument of event \"charge\" because it has no direct semantic relations with the event trigger.",
                "cite_spans": [],
                "ref_spans": [],
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                "section": "Event Extraction for All Types",
                "sec_num": "3.3"
            },
            {
                "text": "Meaning Representations Table 7 assesses the impact of various types of semantic information, and also compares the effectiveness of each type of meaning representation for the typing task only. We note that F-measure drops 14.4 points if only WSD based embeddings are not used. In addition, AMR relations specifying both core and non-core roles are informative for learning distinct compositional operators. To compare typing results across meaning representations, we use triggers identified by both the AMR and FrameNet parsers. Using Stanford Typed Dependencies, relations are likely too coarse-grained or lack sufficient semantic information. Thus, our approach cannot leverage the inter-dependency between event trigger type and argument role to achieve pure trigger clusters. Compared with dependency relations, the fine-grained AMR semantic relations such as :location, :manner, :topic, :instrument appear to be more informative to infer the argument roles. For example, in sentence \"Approximately 25 kilometers southwest of Sringar 2 militants were killed in a second gun battle.\", \"gun\" is identified as an Instrument for \"battle\" event based on the AMR relation :instrument. In contrast, dependency parsing identifies \"gun\" as a Table 7 : Impact of semantic information and representations on typing for ERE data.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 24,
                        "end": 31,
                        "text": "Table 7",
                        "ref_id": null
                    },
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                        "start": 1240,
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                        "text": "Table 7",
                        "ref_id": null
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                ],
                "eq_spans": [],
                "section": "Impact of Semantic Information and",
                "sec_num": "3.4"
            },
            {
                "text": "compound modifier of \"battle\". Note that we used a static mapping to map dependency relations to AMR relations (see section 2.6), whereas ideally this mapping would be context-dependent. Creating a context-dependent mapping would constitute significant steps toward building an AMR parser.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Impact of Semantic Information and",
                "sec_num": "3.4"
            },
            {
                "text": "Using FrameNet results in low recall for argument typing. SEMAFOR's output often does not identify all the arguments identified by our annotators. Many triggers are associated with zero or one argument, thus there is not enough data to learn the event structure representation. In addition, most of the arguments from identified by SEMAFOR are long phrases. Because no internal structure is assigned, we simply average all single token's vectors to represent the phrase. However, the high precision may be due to the fact that FrameNet relations are designed to specify semantic roles.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Impact of Semantic Information and",
                "sec_num": "3.4"
            },
            {
                "text": "We manually select the event triggers in the ACE and ERE evaluation sets discovered by our AMRbased approaches that are ACE/ERE events based on their annotation guidelines. If a trigger doesn't already have a gold standard ACE/ERE annotation we provide one. For each such event we use core roles and Instrument/Possessor/Time/Place relations to detect arguments. Each trigger and argument role type is assessed manually if an ACE/ERE annotation does not exist. We evaluate our approach for trigger and argument typing by comparing system output to manual annota-tion, considering synonymous labels to be equivalent (e.g., our approach's kill type ACE's die). We compare our approach with the following state-ofthe-art supervised methods which are trained from 529 ACE documents or 336 ERE documents:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Event Extraction for ACE/ERE Types",
                "sec_num": "3.5"
            },
            {
                "text": "\u2022 DMCNN: A dynamic multi-pooling convolutional neural network based on distributed word representations (Chen et al., 2015) . \u2022 Joint: A structured perceptron model based on symbolic semantic features (Li et al., 2013 ). \u2022 LSTM: A long short-term memory neural network (Hochreiter and Schmidhuber, 1997) based on distributed semantic features. Table 8 shows the results. On ACE events, both DMCNN and Joint methods outperform our approach for trigger and argument extraction. However, when moving to ERE event schema, although re-trained based on ERE labeled data, their performance still degrades significantly. These previous methods heavily rely on the quality and quantity of the training data. When the training data is not adequate (the ERE training documents contain 1,068 events and 2,448 arguments, while ACE training documents contain more than 4,700 events and 9,700 arguments), the performance is low. In contrast, our approach is unsupervised and can automatically identify events, arguments and assign types/roles, and is not tied to one event schema.",
                "cite_spans": [
                    {
                        "start": 104,
                        "end": 123,
                        "text": "(Chen et al., 2015)",
                        "ref_id": "BIBREF10"
                    },
                    {
                        "start": 201,
                        "end": 217,
                        "text": "(Li et al., 2013",
                        "ref_id": "BIBREF25"
                    },
                    {
                        "start": 269,
                        "end": 303,
                        "text": "(Hochreiter and Schmidhuber, 1997)",
                        "ref_id": "BIBREF18"
                    }
                ],
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                    {
                        "start": 344,
                        "end": 351,
                        "text": "Table 8",
                        "ref_id": null
                    }
                ],
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                "section": "Event Extraction for ACE/ERE Types",
                "sec_num": "3.5"
            },
            {
                "text": "To demonstrate the portability of our approach to a new domain, we conduct our experiment on 14 biomedical articles (755 sentences) with perfect AMR annotations (Garg et al., 2016) . We utilize a word2vec model 6 trained from all paper abstracts from PubMed 7 and full-text documents from the PubMed Central Open Access subset. To evaluate the performance, we randomly sample 100 sentences and ask a biomedical scientist to assess the correctness of each event and argument role. Our approach achieves 83.1% precision on trigger labeling (619 events in total) and 78.4% precision on argument labeling (1,124 arguments in total). Table 8 : Performance on ERE and ACE events.",
                "cite_spans": [
                    {
                        "start": 161,
                        "end": 180,
                        "text": "(Garg et al., 2016)",
                        "ref_id": "BIBREF14"
                    }
                ],
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                    {
                        "start": 629,
                        "end": 636,
                        "text": "Table 8",
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                    }
                ],
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                "section": "Event Extraction for Biomedical Domain",
                "sec_num": "3.6"
            },
            {
                "text": "It demonstrates that our approach can be rapidly adapted to a new domain and discover domain-rich event schema. An example schema for an event type \"Dissociate\" is shown in Figure 7 .",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 173,
                        "end": 181,
                        "text": "Figure 7",
                        "ref_id": "FIGREF7"
                    }
                ],
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                "section": "Event Extraction for Biomedical Domain",
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            },
            {
                "text": "man(Agent), Austrialia(Destination),heroin(Theme) t identify the 2 convicts hanged in Zahedan but stated nd guilty of transporting 5.25 kilograms of heroin. rting, Arguments: they(Agent), heroin (Theme) of the facility started in 790000, but stopped after llapse when Tajikistan slid into a 5 year civil war conomy. Event:construction, guments: facility(Product), 790000(Time) t-era military facility was fou-nded in 570000 and all information gathered from Russia's military spy Event: founded, ts: Soviet-era facility(Product), 570000(Time) Event Type: Build S2: A newspaper report on January 1, 2008 that Iran hanged two convicted drug traffickers in the south-eastern city of Zahedan. S1: Colombian Government was alarmed because uranium is the primary basis for generating weapons of mass destruction. Event:alarmed, Arguments:Columbian Government(Experiencer) Event Type: Threaten S2: Cluster bomblets have been criticized by human rights groups because they kill indiscriminately and because unexploded ordinance poses a threat to civilians similar to that of land mines.",
                "cite_spans": [
                    {
                        "start": 195,
                        "end": 202,
                        "text": "(Theme)",
                        "ref_id": null
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                ],
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            {
                "text": "Event:threat, Arguments:ordinance(Cause), civilian(Experiencer) Event: hanged, Arguments: Iran(Agent), drug traffickers(Theme), southeastern city of Zahedan(Place) S1: Ras acts as a molecular switch that is activated upon GTP loading and deactivated upon hydrolysis of GTP to GDP. ",
                "cite_spans": [],
                "ref_spans": [],
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                "section": "Event Extraction for Biomedical Domain",
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            },
            {
                "text": "Most of previous event extraction work focused on learning supervised models based on symbolic features (Ji and Grishman, 2008; Miwa et al., 2009; Liao and Grishman, 2010; Liu et al., 2010; Hong et al., 2011; McClosky et al., 2011; Sebastian and Andrew, 2011; Chen and Ng, 2012; Li et al., 2013) or distributional features through deep learning (Chen et al., 2015; Nguyen and Grishman, 2015) . They usually rely on a predefined event schema and a large amount of training data. Compared with other paradigms such as Open Information Extraction (Etzioni et al., 2005; Banko et al., 2007; Banko et al., 2008; Etzioni et al., 2011; Ritter et al., 2012) , Preemptive IE (Shinyama and Sekine, 2006) , Ondemand IE (Sekine, 2006) and semantic frame based event discovery (Kim et al., 2013) , our approach can explicitly name each event type and argument role. Some recent work focused on universal schema discovery (Chambers and Jurafsky, 2011; Pantel et al., 2012; Yao et al., 2012; Yao et al., 2013; Chambers, 2013; . However, the schemas discovered from these methods are rather static and they are not customized for any specific input corpus. Our work is also related to efforts at composing word embeddings using syntactic structures (Hermann and Blunsom, 2013; Socher et al., 2013a; Socher et al., 2013b; Bowman et al., 2014; . Our trigger sense representation is similar to Word Sense Induction (Navigli, 2009; Bordag, 2006; Pinto et al., 2007; Brody and Lapata, 2009; Manandhar et al., 2010; Navigli and Lapata, 2010; Van de Cruys and Apidianaki, 2011; Wang et al., 2015b) . Besides word sense, we exploit related concepts to enrich trigger representation.",
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                        "text": "Miwa et al., 2009;",
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                        "text": "Sebastian and Andrew, 2011;",
                        "ref_id": "BIBREF45"
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                        "start": 260,
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                        "text": "Chen and Ng, 2012;",
                        "ref_id": "BIBREF9"
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                    {
                        "start": 279,
                        "end": 295,
                        "text": "Li et al., 2013)",
                        "ref_id": "BIBREF25"
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                        "text": "(Chen et al., 2015;",
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                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Related Work",
                "sec_num": "4"
            },
            {
                "text": "We proposed a novel Liberal event extraction framework which combines the merits of symbolic semantics and distributed semantics. Experiments on news and biomedical domain demonstrate that this framework can discover explicitly defined rich event schemas which cover not only most types in existing manually defined schemas, but also new event types and argument roles. The granularity of event types is also customized for specific input corpus. And it can produce high-quality event annotations simultaneously without using annotated training data. In the future, we will extend this framework to other Information Extraction tasks.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusions and Future Work",
                "sec_num": "5"
            },
            {
                "text": "For consistency, we use the same trigger identification procedure regardless of which meaning representation is used to derive semantic relations.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "For relation details, see https://github.com/amrisi/amrguidelines/blob/master/amr.md 4 WordNet-OntoNotes mapping from https://catalog.ldc.upenn.edu/LDC2011T03",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "http://bio.nlplab.org/ 7 http://www.ncbi.nlm.nih.gov/pubmed",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            }
        ],
        "back_matter": [
            {
                "text": "We would like to thank Kevin Knight and Jonathan May (ISI) for sharing biomedical AMR annotations. This work was supported by the U.S. ARL NS-CTA No. W911NF-09-2-0053 and DARPA DEFT No. FA8750-13-2-0041, and in part by NSF IIS-1523198, IIS-1017362, IIS-1320617 and IIS-1354329, and NIH BD2K grant 1U54GM114838. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Acknowledgements",
                "sec_num": null
            }
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        "ref_entries": {
            "FIGREF0": {
                "uris": null,
                "text": "Comparison between ACE Event Extraction and Liberal Event Extraction.",
                "num": null,
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            },
            "FIGREF1": {
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                "text": "Liberal Event Extraction Overview.",
                "num": null,
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            },
            "FIGREF2": {
                "uris": null,
                "text": "illustrates the overall framework of",
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            },
            "FIGREF3": {
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                "text": "Event Trigger and Argument Annotations and AMR Parsing Results of E1.",
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            },
            "FIGREF4": {
                "uris": null,
                "text": "Partial AMR and Event Structure for E2.",
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            },
            "FIGREF6": {
                "uris": null,
                "text": "Joint Constraint Clustering for E3,4,5.",
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                "type_str": "figure"
            },
            "FIGREF7": {
                "uris": null,
                "text": "Event: hydrolysis Arguments:GTP (Patient), (GDP) (Result) Event Type: Dissociate S2: Activation requires dissociation of protein-bound GDP , an intrinsically slow process that is accelerated by guanine nucleotide exchange factors. Event: dissociation Arguments: GDP (Patient) S3: His -ubiquitinated proteins were purified by Co2+ metal affinity chromatography in 8M urea denaturing conditions. Event: denaturing Arguments: proteins(Patient) Example Output of the Discovered Biomedical Event Schema.",
                "num": null,
                "type_str": "figure"
            },
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                "content": "<table><tr><td>Type:</td><td colspan=\"2\">Conflict</td><td>\u2026</td><td>Life</td></tr><tr><td>Subtype:</td><td>Demonstrate</td><td colspan=\"2\">Attack</td><td>Marry Die \u2026 Injure</td></tr><tr><td colspan=\"3\">Argument: Entity Time Place</td><td colspan=\"2\">\u2026 Agent Victim Time \u2026 \u2026</td></tr></table>",
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            "TABREF4": {
                "text": "Event-Related AMR Relations.",
                "type_str": "table",
                "num": null,
                "content": "<table/>",
                "html": null
            },
            "TABREF5": {
                "text": "Cluster bomblets have been criticized by human rights groups because they kill indiscriminately and because unexploded ordinance poses a threat to civilians similar to that of land mines.",
                "type_str": "table",
                "num": null,
                "content": "<table><tr><td/><td/><td/><td/><td>Event: hanged, Arguments: Iran(Agent), drug traf-</td></tr><tr><td/><td/><td/><td/><td>fickers(Theme), southeastern city of Zahedan(Place)</td></tr><tr><td/><td/><td/><td/><td>Event:threat,</td></tr><tr><td/><td/><td/><td colspan=\"2\">Arguments:ordinance(Cause), civilian(Experiencer)</td></tr><tr><td/><td>:poss</td><td/><td/><td/></tr><tr><td/><td/><td>:mod</td><td/><td/></tr><tr><td>:op1</td><td>:op2</td><td/><td>:mod</td><td/></tr><tr><td>Bill</td><td>Bennet</td><td>glam</td><td>gamble</td><td>lose</td></tr></table>",
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            "TABREF7": {
                "text": "Core Role Mapping Examples.",
                "type_str": "table",
                "num": null,
                "content": "<table><tr><td>3 Evaluation</td></tr><tr><td>3.1 Data</td></tr><tr><td>We used the August 11, 2014 English Wikipedia</td></tr><tr><td>dump to learn trigger sense and argument embed-</td></tr><tr><td>dings. For evaluation we choose a subset of ERE</td></tr><tr><td>(Entity Relation Event) corpus (50 documents)</td></tr><tr><td>which has perfect AMR annotations so we can</td></tr></table>",
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            },
            "TABREF8": {
                "text": "None-Core Role Mapping.",
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                "num": null,
                "content": "<table/>",
                "html": null
            },
            "TABREF9": {
                "text": "The court official stated that on 18 March 2008 Luong stated to judges that she was hired by an unidentified man to ship the heroin to Australia in exchange for 15000 U.S. dollars. Event: ship, State media didn't identify the 2 convicts hanged in Zahedan but stated that they had been found guilty of transporting 5.25 kilograms of heroin. Police in the strict communist country discovered his methamphetamine manufacturing plant disguised as a soap factory and sentenced him to death in 1997. Event: death, Arguments: him(Theme), 1997(Time) S2: A newspaper report on January 1, 2008 that Iran hanged two convicted drug traffickers in the south-eastern city of Zahedan. Colombian Government was alarmed because uranium is the primary basis for generating weapons of mass destruction. Cluster bomblets have been criticized by human rights groups because they kill indiscriminately and because unexploded ordinance poses a threat to civilians similar to that of land mines.",
                "type_str": "table",
                "num": null,
                "content": "<table><tr><td/><td colspan=\"2\">Event Type: Transport</td><td>Event Type: Die</td><td/></tr><tr><td colspan=\"3\">Arguments: man(Agent), Austrialia(Destination),heroin(Theme) S2: Event: transporting, Arguments: they(Agent), heroin(Theme) S1: The construction of the facility started in 790000, but stopped after the 910000 Soviet collapse when Tajikistan slid into a 5 year civil war that undermined its economy. Event:construction, Event Type: Build</td><td colspan=\"3\">Event Type: Threaten Event: hanged, Arguments: Iran(Agent), drug traf-S1: Event:alarmed, Arguments:Columbian Government(Experiencer) fickers(Theme), southeastern city of Zahedan(Place)</td></tr><tr><td colspan=\"3\">Arguments: facility(Product), 790000(Time) S2: The closed Soviet-era military facility was fou-nded in 570000 and collects and analyzes all information gathered from Russia's military spy satellites. Event: founded, Arguments: Soviet-era facility(Product), 570000(Time)</td><td colspan=\"2\">S2: Event:threat, Arguments:ordinance(Cause), civilian(Experiencer)</td></tr><tr><td/><td/><td colspan=\"2\">Figure 6: Example Output of the Event Schema.</td><td/></tr><tr><td>Data</td><td colspan=\"5\">Event Type: Dissociate Human SystemAM R Overlap Human P erf ectAM R Overlap SystemAM R Overlap ACE ERE</td></tr><tr><td># of Events</td><td>440</td><td colspan=\"2\">S1: Ras acts as a molecular switch that is activated upon GTP loading and 2,395 331 580 3,765 517</td><td>2,498</td><td>477</td></tr><tr><td># of Event Types # of Arguments</td><td>33 883</td><td colspan=\"2\">deactivated upon hydrolysis of GTP to GDP. 134 N/A 26 Event: hydrolysis Arguments: GTP (Patient), GDP (Result) 137 S2: Activation requires dissociation of protein-bound GDP , an intrinsica-N/A 4,361 587 1,231 6,195 919</td><td>120 4,288</td><td>N/A 801</td></tr><tr><td/><td/><td colspan=\"2\">lly slow process that is accelerated by guanine nucleotide exchange factors.</td><td/></tr><tr><td/><td/><td colspan=\"2\">Event: dissociation Arguments: GDP (Patient)</td><td/></tr><tr><td/><td/><td colspan=\"2\">S3: His -ubiquitinated proteins were purified by Co2+ metal affinity</td><td/></tr><tr><td/><td/><td colspan=\"2\">chromatography in 8M urea denaturing conditions.</td><td/></tr><tr><td/><td/><td colspan=\"2\">Event: denaturing Arguments: proteins(Patient)</td><td/></tr></table>",
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            "TABREF12": {
                "text": "Overall Performance of Liberal Event Extraction on ERE data for All Event Types.",
                "type_str": "table",
                "num": null,
                "content": "<table><tr><td>Method</td><td>Trigger F1 (%) P R F1</td><td>Arg F1 (%) P R F1</td></tr><tr><td>Perfect AMR</td><td colspan=\"2\">70 79.5 74.5 72.4 64.4 68.2</td></tr><tr><td>w/o Structure</td><td colspan=\"2\">52.8 59.4 55.9 52.1 48.0 50.0</td></tr><tr><td>Representation</td><td/><td/></tr><tr><td>w/o WSD based</td><td colspan=\"2\">62.8 57.4 60.1 61.9 50.3 55.5</td></tr><tr><td>embeddings</td><td/><td/></tr><tr><td>w/o None-Core Roles</td><td colspan=\"2\">61.5 72.2 66.5 61.3 58.0 59.6</td></tr><tr><td>w/o Core Roles</td><td colspan=\"2\">57.3 49.7 53.2 63.6 49.5 55.7</td></tr><tr><td>System AMR</td><td colspan=\"2\">69.8 50.5 58.6 68.9 42.9 52.9</td></tr><tr><td>Replace AMR with</td><td colspan=\"2\">45.9 61.9 52.7 63.9 18.2 28.4</td></tr><tr><td>Dependency Parsing</td><td/><td/></tr><tr><td>Replace AMR with</td><td colspan=\"2\">43.1 57.1 49.2 78.1 7.1 13.0</td></tr><tr><td>FrameNet Parsing</td><td/><td/></tr></table>",
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