{ "paper_id": "P93-1033", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T08:52:11.663861Z" }, "title": "AN EMPIRICAL STUDY ON THEMATIC KNOWLEDGE ACQUISITION BASED ON SYNTACTIC CLUES AND HEURISTICS", "authors": [ { "first": "Rey-Long", "middle": [], "last": "Liu", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Tsing-Hua University HsinChu", "location": { "country": "Taiwan, R.O.C" } }, "email": "" }, { "first": "Von-Wun", "middle": [], "last": "Soo", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Tsing-Hua University HsinChu", "location": { "country": "Taiwan, R.O.C" } }, "email": "soo@cs.nthu.edu" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Thematic knowledge is a basis of semamic interpretation. In this paper, we propose an acquisition method to acquire thematic knowledge by exploiting syntactic clues from training sentences. The syntactic clues, which may be easily collected by most existing syntactic processors, reduce the hypothesis space of the thematic roles. The ambiguities may be further resolved by the evidences either from a trainer or from a large corpus. A set of heurist-cs based on linguistic constraints is employed to guide the ambiguity resolution process. When a train,-.r is available, the system generates new sentences wtose thematic validities can be justified by the trainer. When a large corpus is available, the thematic validity may be justified by observing the sentences in the corpus. Using this way, a syntactic processor may become a thematic recognizer by simply derivir.g its thematic knowledge from its own syntactic knowledge.", "pdf_parse": { "paper_id": "P93-1033", "_pdf_hash": "", "abstract": [ { "text": "Thematic knowledge is a basis of semamic interpretation. In this paper, we propose an acquisition method to acquire thematic knowledge by exploiting syntactic clues from training sentences. The syntactic clues, which may be easily collected by most existing syntactic processors, reduce the hypothesis space of the thematic roles. The ambiguities may be further resolved by the evidences either from a trainer or from a large corpus. A set of heurist-cs based on linguistic constraints is employed to guide the ambiguity resolution process. When a train,-.r is available, the system generates new sentences wtose thematic validities can be justified by the trainer. When a large corpus is available, the thematic validity may be justified by observing the sentences in the corpus. Using this way, a syntactic processor may become a thematic recognizer by simply derivir.g its thematic knowledge from its own syntactic knowledge.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "Natural language processing (NLP) systems need various knowledge including syntactic, semantic, discourse, and pragmatic knowledge in different applications. Perhaps due to the relatively wellestablished syntactic theories and forrc.alisms, there were many syntactic processing systew, s either manually constructed or automatically extenJ~d by various acquisition methods (Asker92, Berwick85, Brentgl, Liu92b, Lytinen90, Samuelsson91, Simmons91 Sanfi-lippo92, Smadja91 and Sekine92) . However, the satisfactory representation and acquisition methods of domain-independent semantic, disco~lrse, and pragmatic knowledge are not yet develo~d or computationally implemented. NLP systems 6f'.en suffer the dilemma of semantic representation. Sophisticated representation of semantics has better expressive power but imposes difficulties on acquF;ition in practice. On the other hand, the poor adequacy of naive semantic representation may deteriorate the performance of NLP systems. Therefore, for plausible acquisition and processing, domain-dependent semantic bias was 9ften employed in many previous acquisition systez, s (Grishman92b, Lang88, Lu89, and Velardi91) .", "cite_spans": [ { "start": 461, "end": 483, "text": "Smadja91 and Sekine92)", "ref_id": null }, { "start": 1121, "end": 1134, "text": "(Grishman92b,", "ref_id": "BIBREF7" }, { "start": 1135, "end": 1142, "text": "Lang88,", "ref_id": "BIBREF12" }, { "start": 1143, "end": 1148, "text": "Lu89,", "ref_id": "BIBREF16" }, { "start": 1149, "end": 1163, "text": "and Velardi91)", "ref_id": "BIBREF30" } ], "ref_spans": [], "eq_spans": [], "section": "INTRODUCTION", "sec_num": "1." }, { "text": "In thi~ paper, we present an implemented system that acquires domain-independent thematic knowledge using available syntactic resources (e.g. syntactic p~acessing systems and syntactically processed cort;ara). Thematic knowledge can represent semantic or conceptual entities. For correct and efficient parsing, thematic expectation serves as a basis for conflict resolution (Taraban88) . For natural language understanding and other applications (e.g. machine translation), thematic role recognition is a major step. ~ematic relations may serve as the vocabulary shared by the parser, the discourse model, and the world knowledge (Tanenhaus89) . More importantly, since thematic structures are perhaps most closely link~d to syntactic structures ($ackendoff72), thematic knowledge acquisition may be more feasible when only .:'yntactic resources are available. The consideration of the availability of the resources from which thematic knowledge may be derived promotes the practica2 feasibility of the acquisition method.", "cite_spans": [ { "start": 374, "end": 385, "text": "(Taraban88)", "ref_id": "BIBREF29" }, { "start": 630, "end": 643, "text": "(Tanenhaus89)", "ref_id": "BIBREF28" } ], "ref_spans": [], "eq_spans": [], "section": "INTRODUCTION", "sec_num": "1." }, { "text": "In geaeral, lexical knowledge of a lexical head should (at ~east) include 1) the number of arguments of the lexic~-~l head, 2) syntactic properties of the arguments, and 3) thematic roles of the arguments (the argument ,:~ructure). The former two components may be eitt~er already constructed in available syntactic processors or acquired by many syntactic acquisition system s . However, the acquisition of the thematic roles of th~ arguments deserves more exploration. A constituent~ay have different thematic roles for different verbs in different uses. For example, \"John\" has different th,~matic roles in (1.1) -(1.4). To acquire thematic lexical knowledge, precise thematic roles of arguments in the sentences needs to be determined.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "INTRODUCTION", "sec_num": "1." }, { "text": "In the next section, the thematic roles considered in this paper are listed. The syntactic properties of the thematic roles are also summarized. The syntactic properties serve as a preliminary filter to reduce the hypothesis space of possible thematic roles of arguments in training sentences. To further resolve the ambiguities, heuristics based on various linguistic phenomena and constraints are introduced in section 3. The heuristics serve as a general guidance for the system to collect valuable information to discriminate thematic roles. Current status of the experiment is reported in section 4. In section 5, the method is evaluated and related to previous methodologies. We conclude, in section 6, that by properly collecting discrimination information from available sources, thematic knowledge acquisition may be, more feasible in practice.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "INTRODUCTION", "sec_num": "1." }, { "text": "The thematic roles considered in this paper and the syntactic clues for identifying them are presented in Table 1 . The syntactic clues include i) the possible syntactic constituents of the arguments, 2) whether animate or inanimate arguments, 3) grammatical functions (subject or object) of the a;guments when they are Noun Phrases (NPs), and 4) p:epositions of the prepositional phrase in which the aaguments may occur, The syntactic constituents inc!t:de NP, Proposition (Po), Adverbial Phrase (ADVP), Adjective Phrase (ADJP), and Prepositional phrase (PP). In addition to common animate nouns (e.g. he, she, and I), proper nguns are treated as animate NPs as well. In Table 1 , \"y\", \"n\", \"?\", and \"-\" denote \"yes\", \"no\", \"don't care\", and \"seldom\" respectively. For example, an Agent should be an animate NP which may be at the subject (but not object) position, and if it is in a PP, the preposition of the PP should be \"by\" (e.g. \"John\" in \"the light is turned on by John\").", "cite_spans": [], "ref_spans": [ { "start": 106, "end": 113, "text": "Table 1", "ref_id": "TABREF0" }, { "start": 672, "end": 679, "text": "Table 1", "ref_id": "TABREF0" } ], "eq_spans": [], "section": "THEMATIC ROLES AND SYNTAC-TIC CLUES", "sec_num": "2." }, { "text": "We consider the thematic roles to be wellknown and referred, although slight differences might be found in various works. The intrinsic properties of the thematic roles had been discussed from various perspectivez in previous literatures (Jackendoff72 and Gruber76). Grimshaw88 -When a tr, lining sentence is entered, arguments of lexical verbs in the sentence need to be extracted before leart ing. This can be achieved by invoking a syntactic processor. Table 2 . Heuristics for discriminating ther atic roles", "cite_spans": [ { "start": 267, "end": 277, "text": "Grimshaw88", "ref_id": "BIBREF5" } ], "ref_spans": [ { "start": 456, "end": 463, "text": "Table 2", "ref_id": null } ], "eq_spans": [], "section": "THEMATIC ROLES AND SYNTAC-TIC CLUES", "sec_num": "2." }, { "text": "\u2022 Volition Heuristic (VH): Purposive constructions (e.g. in order to) an0 purposive adverbials (e.g. deliberately and intentionally) may occur in sentences with Agent arguments (Gruber76).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "THEMATIC ROLES AND SYNTAC-TIC CLUES", "sec_num": "2." }, { "text": "\u2022 Imperative Heuristic OH): Imperatives are permissible only for Agent subjects (Gruber76).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "THEMATIC ROLES AND SYNTAC-TIC CLUES", "sec_num": "2." }, { "text": "\u2022 Thematic Hierarchy Heuristic (THH): Given a thematic hierarchy (from higher to lower) \"Agent > Location, Source, Goal > Theme\", the passive by-phrases must reside at a higher level than the derived subjects in the hierarchy (i.e. the Thematic Hierarchy Condition in Jackendoff72). In this papzr, we set up the hierarchy: Agent > Location, Source, Goal, Instrument, Cause > Theme, Beneficiary, Time, Quantity, Proposition, Manner, Result. Subjects and objects cannot reside at the same level.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "THEMATIC ROLES AND SYNTAC-TIC CLUES", "sec_num": "2." }, { "text": "\u2022 Preposition Heuristic (PH): The prepositions of the PPs in which the arguments occur often convey good discrimination information for resolving thematic roles ambiguities (see the \"Preposition in PP\" column in Table 1 ).", "cite_spans": [], "ref_spans": [ { "start": 212, "end": 219, "text": "Table 1", "ref_id": "TABREF0" } ], "eq_spans": [], "section": "THEMATIC ROLES AND SYNTAC-TIC CLUES", "sec_num": "2." }, { "text": "\u2022 One-Theme Heuristic (OTH): An ~xgument is preferred to be Theme if itis the only possible Theme in the argument structure.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "THEMATIC ROLES AND SYNTAC-TIC CLUES", "sec_num": "2." }, { "text": "\u2022 Uniqueness Heuristic (UH): No twc, arguments may receive the sanle thematic role (exclusive of conjunctions and anaphora which co-relate two constituents assigned with the same thematic role).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "THEMATIC ROLES AND SYNTAC-TIC CLUES", "sec_num": "2." }, { "text": "If the sentence is selected from a syntactically processed corpus (such as the PENN treebank) the arguments may be directly extracted from the corpus. To identify the thematic roles of the arguments, Table 1 is consulted.", "cite_spans": [], "ref_spans": [ { "start": 200, "end": 207, "text": "Table 1", "ref_id": "TABREF0" } ], "eq_spans": [], "section": "THEMATIC ROLES AND SYNTAC-TIC CLUES", "sec_num": "2." }, { "text": "For example, consider (2.1) as the training sentence. Since \"the robber\" is an animate NP with the subject grammatical function, it can only qualify for Ag, Go, So, and Th. Similarly, since \"the bank\" is an inanimate NP with the object grammatical function, it can only satisfy the requirements of Go, So, Th, and Re. Because of the preposition \"of\", \"th~ money\" can only be Th. As a result, after con,;ulting the constraints in Table 1 , \"the robber\", \"the bank\", and \"the money\" can only be {Ag, Go, So, Tb}, {Go, So, Th, Re}, and {Th} respectively. Therefore, although the clues in Table 1 may serve as a filter, lots of thematic role ambiguities still call for other discrimination information and resolution mechanisms.", "cite_spans": [], "ref_spans": [ { "start": 429, "end": 436, "text": "Table 1", "ref_id": "TABREF0" }, { "start": 585, "end": 592, "text": "Table 1", "ref_id": "TABREF0" } ], "eq_spans": [], "section": "THEMATIC ROLES AND SYNTAC-TIC CLUES", "sec_num": "2." }, { "text": "The remaining thematic role ambiguities should be resolved by the evidences from other sources. Trainers and corpora are the two most commonly available sources of the extra information. Interactive acquisition had been applied in various systems in which the oracle from the trainer may reduce most ambiguities (e.g. Lang88, Liu93, Lu89, and Velardi91). Corpus-based acquisition systems may also converge to a satisfactory performance by collecting evidences from a large corpus (e.g. Brent91, Sekine92, Smadja91, and Zernik89). We are concerned with the kinds of information the available sources may contribute to thematic knowledge acquisition.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "The heuristics to discriminate thematic roles are proposed in Table 2 . The heuristics suggest the system the ways of collecting useful information for resolving ambiguities. Volition Heuristic and Imperative Heuriz'jc are for confirming the Agent role, One-Theme Heuristic is for Theme, while Thematic Hierarchy Heuristic, Preposition Heuristic and Uniqueness Heuristic may be used in a general way.", "cite_spans": [], "ref_spans": [ { "start": 62, "end": 69, "text": "Table 2", "ref_id": null } ], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "It sh~ald be noted that, for the purposes of efficient acquisition, not all of the heuristics were identical to the corresponding original linguistic postulations. For example, Thematic Hierarchy Heuristic was motivated by the Thematic Hierarchy Condition (Jackendoff72) but embedded with more constraints to filter ou~ more hypotheses. One-Theme Heuristic was a relaxed version of the statement \"every sentence has a theme\" which might be too strong in many cases (Jack. mdoff87).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "Becaase of the space limit, we only use an example tc illustrate the idea. Consider (2.1) \"The robber rob'~ed the bank of the money\" again. As mentioned above, after applying the preliminary syntactic clues, \"the robber\", \"the bank\", and \"the money\" may be {Ag, Go, So, Th}, {Ge, So, Th, Re}, and {Th} respectively. By applying Uniqueness Heuristic to the Theme role, the argument structure of \"rob\" in the sentence can only be (AS1) \"{Ag, Go, So}, {Go, So, Re}, {Th}\", which means that, the external argument is {Ag, Go, So} and the internal arguments are {Go, So, Re} and {Th}. Based on the intermediate result, Volition Heuristic, Imperative Heuristic, Thematic Hierarchy Heuristic, and Preposition Heuristic could be invoked to further resolve ambiguities.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "Volition Heuristic and Imperative Heuristic ask the learner to verify the validities of:the sentences such as \"John intentionally robbed the bank\" (\"John\" and \"the robber\" matches because they have the same properties considered in Table 1 and Table 2 ). If the sentence is \"accepted\", an Agent is needed for \"rob\". Therefore, the argument structure becomes (AS2) \"{Ag}, {Go, So, Re}, {Th}\"", "cite_spans": [], "ref_spans": [ { "start": 232, "end": 251, "text": "Table 1 and Table 2", "ref_id": "TABREF0" } ], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "Thematic Hierarchy Heuristic guides the learner to test the validity of the passive Form of (2.1). Similarly, since sentences like \"The barb: is robbed by Mary\" could be valid, \"The robber\" is higher than \"the bank\" in the Thematic Hierarchy. Therefore, the learner may conclude that either AS3 or AS4 may be the argument structure of \"rob\": (AS3) \"{Ag}, {Go, So, Re}, {Th}\" (AS4) \"{Go, So}, {Re}, {Th}\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "Preposition Heuristic suggests the learner to to resolve ambiguities based on the prel:ositions of PPs. For example, it may suggest the sys~.em to confirm: The money is from the bank? If sc, \"the bank\" is recognized as Source. The argument structure becomes (AS5) \"{Ag, Go}, {So}, {Th}\". Combining (AS5) with (AS3) or (ASS) with (AS2), the learner may conclude that the arg~rnent structure of\"rob\" is \"{Ag}, {So}, {Th}\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "In summary, as the arguments of lexical heads are entered to the acquisition system, the clues in Table 1 are consulted first to reduce tiae hypothesis space. The heuristics in Table 2 are then invoked to further resolve the ambiguities by coliecting useful information from other sources. The information that the heuristics suggest the system to collect is the thematic validities of the sentences that may help to confirm the target thematic roles.", "cite_spans": [], "ref_spans": [ { "start": 98, "end": 105, "text": "Table 1", "ref_id": "TABREF0" }, { "start": 177, "end": 184, "text": "Table 2", "ref_id": null } ], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "The confirmation information required by Volition Heuristic, Imperative Heuristic. and Thematic Hierarchy Heuristic may come from corpora (and of course trainers as well), while Preposition Heuristic sometimes r, eeds the information only available from trainers. This is because the derivation of new PPs might generate ungrammatical sentences not available in general .:orpora. For example, (3.1) from (2.3) \"The key can open the door\" is grammatical, while (3.2) from (2.5) \"The letter finally reached John\" is ungrammatical.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "(3.1) The door is opened by the key. (3.2) *The letter finally reached to John. Therefore, simple queries as above are preferred in the method.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "It should also be noted that since these heuristics only serve as the guidelines for finding discrimination information, the sequence of their applications does not have significant effects on the result of learning. However, the number of queries may be minimized by applying the heuristics in the order: Volition Heuristic and Imperative Heuristic -> Thematic Hierarchy Heuristic -> Preposition Heuristic. One-Th',~me Heuristic and Uniqueness Heuristic are invoked each time current hypotheses of thematic roles are changed by the application of the clues, Volition Heuristic, Imperative Heuristic, Thematic Hierarchy Heuristic, or Preposition Heuristic. This is because One-Theme Heuristic and Uniqueness Heuristic az'e constraint-based. Given a hypothesis of thematic r~.es, they may be employed to filter out impossible combinations of thematic roles without using any qaeries. Therefore, as a query is issued by other heuristics and answered by the trainer or the corpus, the two heuristics may be used to \"extend\" the result by ft~lher reducing the hypothesis space.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "FINDING EXTRA INFORMATION FOR RESOLVING THETA ROLE AMBIGUITIES", "sec_num": "3." }, { "text": "As described above, the proposed acquisition method requires syntactic information of arguments as input (recall Table 1 ). We believe that the syntactic information is one of the most commonly available resources, it may be collected from a syntactic processor or a ;yntactically processed corpus. To test the method wita a public corpus as in Grishman92a, the PENN Tre~Bank was used as a syntactically processed co~pus for learning. Argument packets (including VP packets and NP packets) were extracted .tom ATIS corpus (including JUN90, SRI_TB, and TI_TB tree files), MARI corpus (including AMBIC~ and WBUR tree files), MUC1 corpus, and MUC2 corpus of the treebank. VP packets and NP packets recorded syntactic properties of the arguments of verbs and nouns respectively. Nouns 1716 138 959 188 1067 509 425 288 1916 732 907 490 6410 1556 3313 1177 Since not all constructions involving movement were tagged with trace information in the corpus, to derive the arguments, the procedure needs to consider the constructions of passivization, interjection, and unbounded dependency (e.g. in relative clauses and wh-questions). That is, it needs to determine whether a constituent is an argument of a verb (or noun), whether an argument is moved, and if so, which constituent is the moved argument. Basically, Case Theory, Theta Theory (Chomsky81), and Foot Feature Principle (Gazdar85) were employed to locate the arguments (Liu92a, Liu92b) . Table 3 summarizes the results of the argument extraction. About 96% of the trees were extracted. Parse trees with too many words (60) or nodes (i.e. 50 subgoals of parsing) were discarded. ~2~1 VP packets in the parse trees were derived, but only the NP packets having PPs as modifiers were extracted. These PPs could help the system to hypothesize axgument structures of nouns. The extracted packets were assimilated into an acquisition system (called EBNLA, Liu92a) as syntactic subcategorization frames. Different morphologies of lexicons were not counted as different verbs and nouns.", "cite_spans": [ { "start": 1439, "end": 1447, "text": "(Liu92a,", "ref_id": "BIBREF13" }, { "start": 1448, "end": 1455, "text": "Liu92b)", "ref_id": "BIBREF14" } ], "ref_spans": [ { "start": 113, "end": 120, "text": "Table 1", "ref_id": "TABREF0" }, { "start": 775, "end": 867, "text": "Nouns 1716 138 959 188 1067 509 425 288 1916 732 907 490 6410 1556 3313 1177", "ref_id": "TABREF0" }, { "start": 1458, "end": 1465, "text": "Table 3", "ref_id": null } ], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "As an example of the extracted argument packets, consider the following sentence from MUCI: \"..., at la linea ..... where a FARC front ambushed an 1 lth brigade army patrol\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "The extraction procedure derived the following VP packet for \"ambushed\": ambushed (NP: a FARC fxont) (WHADVP: where) (NP: an 1 lth brigade army patrol)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "The first NP was the external argument of the verb. Other constituents were internal arga:nents of the verb. The procedure could not determ,r.e whether an argument was optional or not.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "In the corpora, most packets were for a small number of verbs (e.g. 296 packets tot \"show\" were found in ATIS). Only 1 to 2 packets could be found for most verbs. Therefore, although tt.e parse trees could provide good quality of argument packets, the information was too sparse to resoNe, thematic role ambiguities. This is a weakness embedded in most corpus-based acquisition methods, since the learner might finally fail to collect sufficient information after spending much. effort to process the corpus. In that case, the ~ambiguities need to be temporarily suspended. ~To seed-up learning and focus on the usage of the proposed method, a trainer was asked to check the thematic validities (yes/no) of the sentences generated b,, the learner.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "Excluding packets of some special verbs to be discussed later and erroneous packets (due to a small amount of inconsistencies and incompleteness of the corpus and the extraction procedure), the packets were fed into the acquisition system (one packet for a verb). The average accuracy rate of the acquired argument struct~ares was 0.86. An argument structure was counted as correct if it was unambiguous and confirmed by the trainer. On average, for resolving ambiguities, 113 queries were generated for every 100 successfully acquired argument structures. The packets from ATIS caused less ambiguities, since in this corpus there were many imperative sentences to which Impe:ative Heuristic may be applied. Volition Heuristic, Thematic Hierarchy Heuristic, and Preposition Heuristic had almost equal frequencies of application in the experiment.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "As an. example of how the clues and heuristics could successfully derive argument structures of verbs, consider the sentence from ATIS: \"The flight going to San Francisco ...\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "Without issuing any queries, the learner concluded that an argument structure of \"go\" is \"{Th}, {Go}\" This was because, according to the clues, \"San Francisco\" couM only be Goal, while according to One-Theme Heuristic, \"the flight\" was recognized as Theme. Most argument structures were acquired using 1 to ~ queries.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "The result showed that, after (manually or automatically) acquiring an argument packet (i.e. a syntactic s t, bcategorization frame plus the syntactic constituent l 3f the external argument) of a verb, the acquisition~'rnethod could be invoked to upgrade the syntactic knowledge to thematic knowledge by issuing only 113 queries for every 100 argument packets. Since checking the validity of the generated sentences is not a heavy burden for the trainer (answering 'yes' or 'no' only), the method may be attached to various systems for promoting incremental extensibility of thematic knowledge.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "The way of counting the accuracy rate of the acquired argument structures deserves notice. Failed cases were mainly due to the clues and heuristics that were too strong or overly committed. For example, the thematic role of \"the man\" in (4.1) from MARI could not be acquired using the clues and heuristics.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "(4.1) Laura ran away with the man.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "In the terminology of Gruber76, this is an expression of accompaniment which is not considered in the clues and heuristics. As another example, consider (4.2) also from MARI.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "(4.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "2) The greater Boston area ranked eight among major cities for incidence of AIDS.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "The clues and heuristics could not draw any conclusions on the possible thematic roles of \"eight\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "On the other hand, the cases cour.ted as \"failed\" did not always lead to \"erroneous\" argument structures. For example, \"Mary\" in (2.9) \"John promised Mary to marry her\" was treated as Theme rather than Goal, because \"Mary\" is the only possible Theme. Although \"Mary\" may be Theme in this case as well, treating \"Mary\" as Goal is more f'me-grained.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "The clues and heuristics may often lead to acceptable argument structures, even if the argument structures are inherently ambiguous. For example, an NP might function as more than one thematic role within a sentence (Jackendoff87). Ia (4.3), \"John\" may be Agent or Source.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "(4.3) John sold Mary a coat.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "Since Thematic Hierarchy Heuristic assumes that subjects and objects cannot reside at the same level, \"John\" must not be assigned as Sotuce. Therefore, \"John\" and \"Mary\" are assigned as Agent and Goal respectively, and the ambiguity is resolved.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "In addition, some thematic roles may cause ambiguities if only syntactic evidences are available. Experiencer, such as \"John\" in (4.4), arid Maleficiary, such as \"Mary\" in (4.5), are the two examples. There are difficulties in distinguishing Experiencer, Agent, Maleficiary and Theme. Fortunately, the verbs with Experiencer and Maleficiary may be enumerated before learning. Therefore, the argumen,: structures of these verbs are manually constructed rather than learned by the proposed method.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "EXPERIMENT", "sec_num": "4." }, { "text": "To explore the acquisition of domain-independent semantic knowledge, the universal linguistic constraints postulated by many linguistic studies may provide gefieral (and perhaps coarse-grained) hints. The hints may be integrated with domain-specific semantic bias for various applications as well. In the branch of Lhe study, GB theory (Chomsky81) and universal feature instantiation principles (Gazdar85) had been shown to be applicable in syntactic knowledge ,.cquisition (Berwick85, Liu92a, Liu92b) . The proposed method is closely related to those methodolog,.es. The major difference is that, various thematic theories are selected and computationalized for thematic knowledge acquisition. The idea of structural patterns in Montemagni92 is similar to Preposition Heuristic in that the patterns suggest general guidance to information extraction.", "cite_spans": [ { "start": 395, "end": 405, "text": "(Gazdar85)", "ref_id": "BIBREF4" }, { "start": 474, "end": 485, "text": "(Berwick85,", "ref_id": "BIBREF1" }, { "start": 486, "end": 493, "text": "Liu92a,", "ref_id": "BIBREF13" }, { "start": 494, "end": 501, "text": "Liu92b)", "ref_id": "BIBREF14" } ], "ref_spans": [], "eq_spans": [], "section": "RELATED WORK", "sec_num": "5." }, { "text": "Extra information resources are needed for thematic knawledge acquisition. From the cognitive point of view, morphological, syntactic, semantic, contextual (Jacobs88), pragmatic, world knowledge, and observations of the environment (Webster89, Siskind90) .~e all important resources. However, the availability~of the resources often deteriorated the feasibility of learning from a practical standpoint. The acquisition often becomes \"circular\" when relying on semantic information to acquire target semantic informatmn.", "cite_spans": [ { "start": 232, "end": 243, "text": "(Webster89,", "ref_id": "BIBREF31" }, { "start": 244, "end": 254, "text": "Siskind90)", "ref_id": "BIBREF26" } ], "ref_spans": [], "eq_spans": [], "section": "RELATED WORK", "sec_num": "5." }, { "text": "Prede~:ined domain linguistic knowledge is another important information for constraining the hypothesis ,space in learning (or for semantic bootstrapping). From this point of view, lexical categories (Zernik89, Zemik90) and theory of lexical semantics (Pustejovsky87a, Pustejovsky87b) played similar role~ as the clues and heuristics employed in this paper. The previous approaches had demonstrated the\u00a2::etical interest, but their performance on large-scale acquisition was not elaborated. We feel that, requ~,ng the system to use available resources only (i.e, .,;yntactic processors and/or syntactically processed c'orpora) may make large-scale implementations more feasible. The research investigates the issue as to l what extent an acquisition system may acquire thematic knowledge when only the syntactic resources a:e available.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "RELATED WORK", "sec_num": "5." }, { "text": "McClelland86 showed a connectionist model for thematic role assignment. By manually encoding training ass!gnments and semantic microfeatures for a limited number of verbs and nouns, the connectionist network learned how to assign roles. Stochastic approaches (Smadja91, Sekine92) also employed available corpora to acquire collocational data for resolving ambiguities in parsing. However, they acquired numerical values by observing the whole training corpus (non-incremental learning). Explanation for those numerical values is difficult to derive in those models. As far as the large-scale thematic knowledge acquisition is concerned, the incremental extensibility of the models needs to be further improved.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "RELATED WORK", "sec_num": "5." }, { "text": "Preliminary syntactic analysis could be achieved by many natural language processing systems. Toward semantic interpretation on input sentences, thematic lexical knowledge is needed. Although each lexicon may have its own idiosyncratic thematic requirements on arguments, there exist syntactic clues for hypothesizing the thematic roles of the arguments. Therefore, exploiting the information derived from syntactic analysis to acquire thematic knowledge becomes a plausible way to build an extensible thematic dictionary. In this paper, various syntactic clues are integrated to hypothesize thematic roles of arguments in training sentences. Heuristics-guided ambiguity resolution is invoked to collect extra discrimination information from the nainer or the corpus. As more syntactic resources become available, the method could upgrade the acquired knowledge from syntactic level to thematic level.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "CONCLUSION", "sec_num": "6." } ], "back_matter": [ { "text": "This research is supported in part by NSC (National Science Council of R.O.C.) under the grant NSC82-0408-E-007-029 and NSC81-0408-E007-19 from which we obtained the PENN TreeBank by Dr. Hsien-Chin Liou. 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(1.2) [Goal rohn] inherited a million dollars. (1.3) The magic wand turned [Theme John] into a frog." }, "FIGREF1": { "num": null, "type_str": "figure", "uris": null, "text": "Mary surprised John. (4.5) Mary suffers a headache." }, "TABREF0": { "content": "
Theta roleConstituentAnimateSubjectObjectPreposition in PP
Agent(Ag) Goal(Go)NP NPY y(animate)by till,untill,to,into,down
Source(So)NPy(animate)from
Instrument(In)NPy(no Ag)with,by
Theme(Th)NPYof, about
Beneficiary(Be)NPnfor
Location(Lo)NP,ADJPYat,in,on,under
Time(Ti)NP(Ti)Yat,in,before,after,about,by,on,during
Quantity(Qu)NP(Qu)for
Proposition(Po)Propositionnnone
Manner(Ma)ADVP,PPnin,with
Cause(Ca)NPnby,for,because of
Result(Re)NPYin ,into
", "text": "Syntactic clues for hypothesizing thematic roles", "html": null, "num": null, "type_str": "table" } } } }