{ "paper_id": "P98-1011", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T09:18:23.945513Z" }, "title": "Evaluating a Focus-Based Approach to Anaphora Resolution*", "authors": [ { "first": "Saliha", "middle": [], "last": "Azzam", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Sheffield Regent Court", "location": { "addrLine": "Portobello Road Sheffield", "postCode": "S1 4DP", "country": "UK" } }, "email": "" }, { "first": "Kevin", "middle": [], "last": "Humphreys", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Sheffield Regent Court", "location": { "addrLine": "Portobello Road Sheffield", "postCode": "S1 4DP", "country": "UK" } }, "email": "" }, { "first": "Robert", "middle": [], "last": "Gaizauskas", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Sheffield Regent Court", "location": { "addrLine": "Portobello Road Sheffield", "postCode": "S1 4DP", "country": "UK" } }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "We present an approach to anaphora resolution based on a focusing algorithm, and implemented within an existing MUC (Message Understanding Conference) Information Extraction system, allowing quantitative evaluation against a substantial corpus of annotated real-world texts. Extensions to the basic focusing mechanism can be easily tested, resulting in refinements to the mechanism and resolution rules. Results show that the focusing algorithm is highly sensitive to the quality of syntactic-semantic analyses, when compared to a simpler heuristic-based approach.", "pdf_parse": { "paper_id": "P98-1011", "_pdf_hash": "", "abstract": [ { "text": "We present an approach to anaphora resolution based on a focusing algorithm, and implemented within an existing MUC (Message Understanding Conference) Information Extraction system, allowing quantitative evaluation against a substantial corpus of annotated real-world texts. Extensions to the basic focusing mechanism can be easily tested, resulting in refinements to the mechanism and resolution rules. Results show that the focusing algorithm is highly sensitive to the quality of syntactic-semantic analyses, when compared to a simpler heuristic-based approach.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "Anaphora resolution is still present as a significant linguistic problem, both theoretically and practically, and interest has recently been renewed with the introduction of a quantitative evaluation regime as part of the Message Understanding Conference (MUC) evaluations of Information Extraction (IE) systems (Grishman and Sundheim, 1996) . This has made it possible to evaluate different (implementable) theoretical approaches against sizable corpora of real-world texts, rather than the small collections of artificial examples typically discussed in the literature.", "cite_spans": [ { "start": 312, "end": 341, "text": "(Grishman and Sundheim, 1996)", "ref_id": "BIBREF5" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "This paper describes an evaluation of a focusbased approach to pronoun resolution (not anaphora in general), based on an extension of Sidner's algorithm (Sidner, 1981) proposed in (Azzam, 1996) , with further refinements from development on real-world texts. The approach is implemented within the general coreference mechanism provided by the LaSIE (Large Scale Information Extraction) system (Gaizauskas et al., 1995) and , Sheffield University's entry in the MUC-6 and 7 evaluations.", "cite_spans": [ { "start": 153, "end": 167, "text": "(Sidner, 1981)", "ref_id": "BIBREF9" }, { "start": 180, "end": 193, "text": "(Azzam, 1996)", "ref_id": "BIBREF2" }, { "start": 394, "end": 419, "text": "(Gaizauskas et al., 1995)", "ref_id": "BIBREF4" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "The term focus, along with its many relations such as theme, topic, center, etc., reflects an intuitive notion that utterances in discourse are usually 'about' something. This notion has been put to use in accounts of numerous linguistic phenomena, but it has rarely been given a firm enough definition to allow its use to be evaluated. For anaphora resolution, however, stemming from Sidner's work, focus has been given an algorithmic definition and a set of rules for its application. Sidner's approach is based on the claim that anaphora generally refer to the current discourse focus, and so modelling changes in focus through a discourse will allow the identification of antecedents.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Focus in Anaphora Resolution", "sec_num": "2" }, { "text": "The algorithm makes use of several focus registers to represent the current state of a discourse: CF, the current focus; AFL, the alternate focus list, containing other candidate foci;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Focus in Anaphora Resolution", "sec_num": "2" }, { "text": "and FS, the focus stack. A parallel structure to the CF, AF the actor focus, is also set to deal with agentive pronouns. The algorithm updates these registers after each sentence, confirming or rejecting the current focus. A set of Interpretation Rules (IRs) applies whenever an anaphor is encountered, proposing potential antecedents from the registers, from which one is chosen using other criteria: syntactic, semantic, inferential, etc.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Focus in Anaphora Resolution", "sec_num": "2" }, { "text": "Sidner's algorithmic account, although not exhaustively specified, has lead to the implementation of focus-based approaches to anaphora resolution in several systems, e.g. PIE (Lin, 1995) . However, evaluation of the approach has mainly consisted of manual analyses of small sets of problematic cases mentioned in the literature. Precise evaluation over sizable corpora of real-world texts has only recently become possible, through the resources provided as part of the MUC evaluations.", "cite_spans": [ { "start": 176, "end": 187, "text": "(Lin, 1995)", "ref_id": "BIBREF8" } ], "ref_spans": [], "eq_spans": [], "section": "Evaluating Focus-Based Approaches", "sec_num": "2.1" }, { "text": "The LaSIE system and , has been designed as a general purpose IE system which can conform to the MUC task specifications for named entity identification, coreference resolution, IE template element and relation identification, and the construction of scenario-specific IE templates. The system is basically a pipeline architecture consisting of tokenisation, sentence splitting, part-of-speech tagging, morphological stemming, list lookup, parsing with semantic interpretation, proper name matching, and discourse interpretation. The latter stage constructs a discourse model, based on a predefined domain model, using the, often partial, semantic analyses supplied by the parser.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Coreference in LaSIE", "sec_num": "3" }, { "text": "The domain model represents a hierarchy of domain-relevant concept nodes, together with associated properties. It is expressed in the XI formalism (Gaizauskas, 1995) which provides a basic inheritance mechanism for property values and the ability to represent multiple classificatory dimensions in the hierarchy. Instances of concepts mentioned in a text are added to the domain model, populating it to become a text-, or discourse-, specific model.", "cite_spans": [ { "start": 147, "end": 165, "text": "(Gaizauskas, 1995)", "ref_id": "BIBREF4" } ], "ref_spans": [], "eq_spans": [], "section": "Coreference in LaSIE", "sec_num": "3" }, { "text": "Coreference resolution is carried out by attempting to merge each newly added instance, including pronouns, with instances already present in the model. The basic mechanism is to examine, for each new-old pair of instances: semantic type consistency/similarity in the concept hierarchy; attribute value consistency/similarity, and a set of heuristic rules, some specific to pronouns, which can act to rule out a proposed merge. These rules can refer to various lexical, syntactic, semantic, and po-sitional information about instances. The integration of the focus-based approach replaces the heuristic rules for pronouns, and represents the use of LaSIE as an evaluation platform for more theoretically motivated algorithms. It is possible to extend the approach to include definite NPs but, at present, the existing rules are retained for non-pronominal anaphora in the MUC coreference task: proper names, definite noun phrases and bare nouns.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Coreference in LaSIE", "sec_num": "3" }, { "text": "Pronoun Resolution in LaSIE Our implementation makes use of the algorithm proposed in (Azzam, 1996) , where elementary events (EEs, effectively simple clauses) are used as basic processing units, rather than sentences. Updating the focus registers and the application of interpretation rules (IRs) for pronoun resolution then takes place after each EE, permitting intrasentential references3 In addition, an initial 'expected focus' is determined based on the first EE in a text, providing a potential antecedent for any pronoun within the first EE.", "cite_spans": [ { "start": 86, "end": 99, "text": "(Azzam, 1996)", "ref_id": "BIBREF2" } ], "ref_spans": [], "eq_spans": [], "section": "Implementing Focus-Based", "sec_num": "4" }, { "text": "Development of the algorithm using realworld texts resulted in various further refinements to the algorithm, in both the IRs and the rules for updating the focus registers. The following sections describe the two rules sets separately, though they are highly interrelated in both development and processing.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Implementing Focus-Based", "sec_num": "4" }, { "text": "The algorithm includes two new focus registers, in addition to those mentioned in section 2: AFS, the actor focus stack, used to record previous AF (actor focus) values and so allow a separate set of IRs for agent pronouns (animate verb subjects); and Intra-AFL, the intrasentential alternate focus list, used to record candidate foci from the current EE only.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Updating the Focus", "sec_num": "4.1" }, { "text": "In the space available here, the algorithm is best described through an example showing the use of the registers. This example is taken from a New York Times article in the MUC-7 training corpus on aircraft crashes:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Updating the Focus", "sec_num": "4.1" }, { "text": "1An important limitation of Sidner's algorithm, noted in (Azzam, 1996) , is that the focus registers are only updated after each sentence. Thus antecedents proposed for an anaphor in the current sentence will always be from the previous sentence or before and intrasentential references axe impossible.", "cite_spans": [ { "start": 57, "end": 70, "text": "(Azzam, 1996)", "ref_id": "BIBREF2" } ], "ref_spans": [], "eq_spans": [], "section": "Updating the Focus", "sec_num": "4.1" }, { "text": "State Police said witnesses told them the propeller was not turning as the plane descended quickly toward the highway in Wareham near Exit 2. It hit a tree.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Updating the Focus", "sec_num": "4.1" }, { "text": "An 'expected focus' algorithm applies to initialise the registers as follows: CF (current focus) = tell_event AF (actor focus) = State Police Intra-AFL remains empty because EE-1 contains no other candidate foci. No other registers are affected by the expected focus. No pronouns occur in EE-1 and so no IRs apply.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EE-I: State Police said tell_event", "sec_num": null }, { "text": "The Intra-AFL is first initialised with all (non-pronominal) candidate foci in the EE: Intra-AFL = witnesses The IRs are then applied to the first pronoun, them, and, in this case, propose the current AF, State Police, as the antecedent. The Intra-AFL is immediately updated to add the antecedent: Intra-AFL = State Police, witnesses EE-2 has a pronoun in 'thematic' position, 'theme' being either the object of a transitive verb, or the subject of an intransitive or the copula (following (Gruber, 1976) ). Its antecedent therefore becomes the new CF, with the previous value moving to the FS. EE-2 has an 'agent', where this is an animate verb subject (again as in (Gruber, 1976) ), and this becomes the new AF. Because the old AF is now the CF, it is not added to the AFS as it would be otherwise. After each EE the Intra-AFL is added to the current AFL, excluding the CF. Non-agent pronouns:", "cite_spans": [ { "start": 490, "end": 504, "text": "(Gruber, 1976)", "ref_id": "BIBREF6" }, { "start": 667, "end": 681, "text": "(Gruber, 1976)", "ref_id": "BIBREF6" } ], "ref_spans": [], "eq_spans": [], "section": "EE-2: witnesses told them", "sec_num": null }, { "text": "(e.g. them in EE-2 above and it in EE-5) CF proposed initially, then members of the AFL and FS.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EE-2: witnesses told them", "sec_num": null }, { "text": "(e.g. their in the brothers had left and were on their way home) Antecedents proposed from the Intra-AFL, allowing intra-EE references.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Possessive, reciprocal and reflexive pronouns (PRRs):", "sec_num": null }, { "text": "Antecedents proposed by the IRs are accepted or rejected based on their semantic type and feature compatibility, using the semantic and attribute value similarity scores of LaSIE's existing coreference mechanism.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Possessive, reciprocal and reflexive pronouns (PRRs):", "sec_num": null }, { "text": "As part of MUC (Grishman and Sundheim, 1996) , coreference resolution was evaluated as a sub-task of information extraction, which involved negotiating a definition of coreference relations that could be reliably evaluated. The final definition included only 'identity' relations between text strings: proper nouns, common nouns and pronouns. Other possible coreference relations, such as 'part-whole', and nontext strings (zero anaphora) were excluded.", "cite_spans": [ { "start": 15, "end": 44, "text": "(Grishman and Sundheim, 1996)", "ref_id": "BIBREF5" } ], "ref_spans": [], "eq_spans": [], "section": "Evaluation with the MUC Corpora", "sec_num": "5" }, { "text": "The definition was used to manually annotate several corpora of newswire texts, using SGML markup to indicate relations between text strings. Automatically annotated texts, produced by systems using the same markup scheme, were then compared with the manually annotated versions, using scoring software made available to MUC participants, based on (Vilain et al., 1995) .", "cite_spans": [ { "start": 348, "end": 369, "text": "(Vilain et al., 1995)", "ref_id": "BIBREF11" } ], "ref_spans": [], "eq_spans": [], "section": "Evaluation with the MUC Corpora", "sec_num": "5" }, { "text": "The scoring software calculates the standard Information Retrieval metrics of 'recall' and 'precision', 2 together with an overall f-measure. The following section presents the results obtained using the corpora and scorer provided for MUC-7 training (60 texts, average 581 words per text, 19 words per sentence) and evaluation (20 texts, average 605 words per text, 20 words per sentence), the latter provided for the formal MUC-7 run and kept blind during development.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Evaluation with the MUC Corpora", "sec_num": "5" }, { "text": "The MUC scorer does not distinguish between different classes of anaphora (pronouns, definite noun phrases, bare nouns, and proper nouns), but baseline figures can be established by running the LaSIE system with no attempt made to resolve any pronouns:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Results", "sec_num": "6" }, { "text": "Recall Precision f showing that more than three quarters of the estimated 20% of pronoun coreferences in the corpora are correctly resolved with only a minor loss of precision. LaSIE with the focus-based algorithm achieves the following:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "~Recall is a measure of how many correct (i.e. manually annotated) coreferences a system found, and precision is a measure of how many coreferences that the system proposed were actually correct. For example, with 100 manually annotated coreference relations in a corpus and a system that proposes 75, of which 50 are correct, recall is then 50/100 or 50% and precision is 50/75 or 66.7%.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "Recall Precision which, while demonstrating that the focusbased algorithm is applicable to real-world text, does question whether the more complex algorithm has any real advantage over LaSIE's original simple approach. The lower performance of the focus-based algorithm is mainly due to an increased reliance on the accuracy and completeness of the grammatical structure identified by the parser. For example, the resolution of a pronoun will be skipped altogether if its role as a verb argument is missed by the parser. Partial parses will also affect the identification of EE boundaries, on which the focus update rules depend. For example, if the parser fails to attach a prepositional phrase containing an antecedent, it will then be missed from the focus registers and so the IRs (see (Azzam, 1995) ). The simple LaSIE approach, however, will be unaffected in this case.", "cite_spans": [ { "start": 790, "end": 803, "text": "(Azzam, 1995)", "ref_id": "BIBREF1" } ], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "Recall is also lost due to the more restricted proposal of candidate antecedents in the focusbased approach. The simple LaSIE approach proposes antecedents from each preceding paragraph until one is accepted, while the focusbased approach suggests a single fixed set.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "From a theoretical point of view, many interesting issues appear with a large set of examples, discussed here only briefly because of lack of space.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "Firstly, the fundamental assumption of the focus-based approach, that the focus is favoured as an antecedent, does not always apply. For example:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "In June, a few weeks before the crash of TWA Flight 800, leaders of several Middle Eastern terrorist organizations met in Teheran to plan terrorist acts.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "Among them was the PFL of Palestine, an organization that has been linked to airplane bombings in the past.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "Here, the pronoun them corefers with organizations rather than the focus leaders. Additional information will be required to override the fundamental assumption.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "Another significant question is when sentence focus changes. In our algorithm, focus changes when there is no reference (pronominal or otherwise) to the current focus in the current EE. In the example used in section 4.1, this causes the focus at the end of the first sentence to be that of the last EE in that sentence, thus allowing the pronoun it in the subsequent sentence to be correctly resolved with the plane.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "However in the example below, the focus of the first EE (the writ) is the antecedent of the pronoun it in the subsequent sentence, rather than the focus from the last EE (the ...flight):", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "The writ is for \"damages\" of seven passengers who died when the Airbus A310 flight crashed. It claims the deaths were caused by negligence.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "Updating focus after the complete sentence, rather than each EE, would propose the correct antecedent in this case. However neither strategy has a significant overall advantage in our evaluations on the MUC corpora.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "Another important factor is the priorities of the Interpretation Rules. For example, when a personal pronoun can corefer with both CF and AF, IRs select the CF first in our algorithm.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "However, this priority is not fixed, being based only on the corpora used so far, which raises the possibility of automatically acquiring IR priorities through training on other corpora.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus", "sec_num": null }, { "text": "A focus-based approach to pronoun resolution has been implemented within the LaSIE IE system and evaluated on real-world texts. The results show no significant preformance increase over a simpler heuristic-based approach. The main limitation of the focus-based approach is its reliance on a robust syntactic/semantic analysis to find the focus on which all the IRs depend. Examining performance on the realworld data also raises questions about the theoretical assumptions of focus-based approaches, in particular whether focus is always a favoured antecedent, or whether this depends, to some extent, on discourse style.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conclusion", "sec_num": "7" }, { "text": "Analysing the differences in the results of the focus-and non-focus-based approaches, does show that the focus-based rules are commonly required when the simple syntactic and semantic rules propose a set of equivalent antecedents and can only select, say, the closest arbitrarily. A combined approach is therefore suggested, but whether this would be more effect-ive than further refining the resolution rules of the focus-based approach, or improving parse results and adding more detailed semantic constraints, remains an open question.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conclusion", "sec_num": "7" } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Coreference resolution in a multilingual information extraction system", "authors": [ { "first": "S", "middle": [], "last": "Azzam", "suffix": "" }, { "first": "K", "middle": [], "last": "Humphreys", "suffix": "" }, { "first": "R", "middle": [], "last": "Gaizauskas", "suffix": "" } ], "year": 1998, "venue": "Proceedings of the First Language Resources and Evaluation Conference (LREC). Linguistic Coreference Workshop", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "S. Azzam, K. Humphreys, and R. Gaizauskas. 1998. 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FS = State Police, tell_event
AFL = propeller, witnesses
EE-4: the plane descended
Intra-AFL = the plane
CF = the plane (theme)
AF = witnesses (unchanged)
FS = propeller, State Police, tell_event
AFL = the plane, propeller, witnesses
In the current algorithm the AFL is reset at
this point, because EE-4 ends the sentence.
EE-5: it hit a tree
Intra-AFL = a tree
The IRs resolve the pronoun it with the CF:
CF = the plane (unchanged)
AF = witnesses (unchanged)
FS = propeller, State Police, tell_event
AFL = a tree
4.2 Interpretation Rules
Pronouns are divided into three classes, each
with a distinct set of IRs proposing antecedents:
Personal pronouns acting as agents (an-
imate subjects):(e.g.
The state after EE-2 is then:
CF = State PoliceAF = witnesses
FS = tell_eventAFL = witnesses
EE-3: the propeller was not turning
The Intra-AFL is reinitialised with candidate
foci from this EE:
Intra-AFL = propeller
CF = propeller
AF = witnesses
", "type_str": "table", "html": null, "text": "No pronouns occur in EE-3 and so no IRs apply. The 'theme', propeller here because of the copula, becomes the new CF and the old one is added to the FS. The AF remains unchanged as the current EE lacks an agent:" } } } }