Chang Jason J. S.

Chen Mathis Huey-chyun

English

Chen, Mathis Huey-chyun

49132

Chang, Jason J. S.

49133

Waseda University Repository

Structual Ambiguity and Conceptural Information RetrievalMathis Huey-chyun Chen and Jason J. S. ChangDepartment of Computer ScienceNational Tsing Hua UniversityHsinchu 30043, Taiwandr804311, jschang )@cs.nthu.edu.twtelephone: +886-35-731069 fax: +886-35-723694AbstractMany researches found lexical preferences to be critical in resolving attachmentambiguity [WFB 90][FBK 82][ MP 80]. Most notably, information from Verb-Obj-Prep-Nounstructures (VOPN) has been used to show that LA is very effective in the resolution of PP-attachment ambiguity [HR 93].We investigated extensions to the lexical association strategy. The extensions includeusing conceptual association and acquiring the association information from different kind oflexical relations not limited to relations in VOPN structures. We refer to this approach asDeepAttach. Thus, it is possible to take information from all kinds of syntactical structures aslong as they are alternations of a common deep structure [PU93] related to that implied by theintended attachment.A collection of sense-disambiguated sentences serves as the source of conceptualrelations. No pre-processing is done to find the conceptual relations in these sentences. Instead,information retrieval technique is used to retrieve conceptually most relevant sentences usingthe words from the ambiguous structure as query. The prepositional phrase is then attached infavor of the constituent that has more conceptual presence in the ranked retrieved sentences.An experiment was implemented to embody the idea. The result shows that 75% of PP's in260 VOPN structures can be attached correctly, when simple lexical relevance was considered.1. IntroductionMany researches found lexical preferences to be critical in resolving attachmentambiguity. Jenson and Binot [JB 87] propose to use dictionary definition for disambiguation.[HR 93] describe how co-occurrence of verbs and nouns with prepositions in corpus can beused as an indication of lexical preference. However, dictionary text especially the definitionsare typically uneven in their coverage and inconsistent in the usage of words, while lexical co-occurrence suffers from sparseness of data. Resnik and Hearst (1994) find that using classinformation did not yield improved performance over lexical association due to multiplicity ofclasses for a word and lack of disambiguation.Our proposal is to use sense-disambiguated, sizable body of text as the source of lexicalpreference. Thus, for example, in the sentence 'He woke up to find the house on fire,' one ofthe word senses of the noun 'fire' occurs frequently in the context of the words which areconceptually similar to 'house.' This is evidence of a conceptual association of the direct object`house' with the prepositional object 'fire.' Unlike in previous proposals [HR 93][RH 93],these co-occurrences of concepts need not in the Verb-Object-Preposition relation.1152. The DeepAttach AlgorithmFor simplicity, the verb, direct object, preposition, and head of prepositional object ofeach testing sentence are fed to the prepositional phrase disambiguator. Category of LongmanLEXICON [LO 92] is used as our semantic tag set. Given the sentence 'He woke up to findthe house on fire,' the word 'fire' has four different semantic tags, and the other two wordshave only one tag, respectively.He woke	 up to find	 the house on fire.N1361	 Da007	 Hc075, Hd120, Hh245, Jh212Thus we look into our database which is composed of 25,000 automatically sense-disambiguated sentences [KC 95] to retrieve and rank sentences that are semantically similar tothe testing sentence. Only the pairs of verb/preposition, direct-object/preposition, verb/direct-object, verb/prepositional-object, and direct-object/prepositional-object of the testing sentenceare examined. Note that we only utilize the part-of-speech and semantic information of eachword of the 25,000 sentences in the database. Syntactical relations such as verb/objectrelations are not used.We use the above example to illustrate our idea. Conceptually, we use the followingquery to retrieve relavant sentences from the database. Notice that this query pattern containsboth the tags of detailed level such as `13g082' and of coarse level such as `Bg'. Therefore, thequery will not be too narrow; some sentences will always be retrieved.[('find' and 'on') or ('house' and 'on') ] or[(N1361 and 'on') or (Da007 and 'on')] or[(N1361 and (Hc075 or Hd120 or Hh245 or Jh212)) or(Da007 and (Hc075 or Hd120 or Hh245 or Jh212))) or[(N1 and (Hc or Hd or Hh or Jh)) or(Da and (Hc or Hd or Hh or Jh))]To process the query more efficiently, we create indexes of the database similar to theinverted file used in information retrieval.word,	 iwordb	Isensea	 Isenseb taga	 Ragb	 Ipositiona	 Ipositionb	Isentence#I catch Gh280 De098 pron v 1 2 1I a Gh280 Nd098 pron det 1 3 1catch a De098 Nd098 v det 2 3 1I fish Gh280 Ea017 pron n 1 4 1catch fish De098 Ea017 v n 2 4 1a fish Nd098 Ea017 det n 3 4 11 yesterday Gh280 Lh225 pron n 1 5 1catch yesterday De098 Lh225 v n 2 5 1a yesterday Nd098 Lh225 det n 3 5 1fish yesterday Ea017 Lh225 n n 4 5 1The query returns a list of sentences. We show a few examples in the following.1. exact match at the detailed level:The house is on fire. (subject/predicative-pp)116Da007	 Hc0752. match at both levels:The house is burning! (subject/verb)Da007	 Hc0763. exact match at the coarse level:The building burnt down to ashes. (subject/verb and subject/p-object)Da003	 Hc076	 Hc084It leads to a quick conclusion that the pair 'house-fire' is better than other possible pairssuch as 'woke-fire,' and 'find-fire.' And the prepositional phrase 'on fire' is more likely toattach to the preceding noun phrase rather than to the verb. Besides, there is one by-productthat the word 'fire' can be disambiguated as to have the word sense of `11c075' in the testingsentence.To be quantitative, a ranking function must be designed to denote the degree ofresemblance and compatibility both in structure and in semantics.3. Experiment and ResultsAn experiment was implemented to embody the idea. Currently, only simple lexicalrelevance was considered.To perform an outside test, we select 260 sentences not in the database and manuallymark the verb, direct object, preposition, and head of prepositional object for easy processing.Two human judges are arranged to examine the results. It shows that 75% of PP's in these 260sentences can be correctly attached.4. Analysis and DiscussionExamining those errors, we found some interesting issues.Our original idea is to blend the data extracted from different structures to solve theproblem of data deficiency. However, it will also introduce some kind of noises. For example,our model incorrectly attaches the PP to the Direct-Object for 'acquire knowledge of languageby studying' because knowledge, language, and studying are highly-related in their senses.We observe that the resulting attachment implies an intrinsic Subject/Object orSubject/Predicate relation but most retrieved evidences from the database are of incompatiblePredicate/Object structure. This seems to suggest that the compatibility between structures ofthe query and the database entry should be considered.Another issue that we should consider is the diversity of the prepositions. Differentprepositions have different tendencies of attachment. therefore present different degree ofdifficulty. See Figure 1. for details. This suggests that each preposition should be handleddifferently.5. Ongoing WorkIn this preliminary experiment, the ranking function of a sentence is simply based oncounting. There are many possible ways to refine it. Furthermore, we only take lexical andsemantic information into account. The polarity and the distance between the lexical items are117left out. We are currently constructing a more elaborated model that will handles these twoaspects and the structural compatibility as well.Besides, we assume that the sense-disambiguated database is 100% correct. In fact, theprecision is only about 80%. This inaccuracy in the sense tags obviously has a negative effectin the results of our experiments. We are simultaneously improving the accuracy of sensedisambiguation.50454035302520151050                            M Correct CountII Error Count      I ill 1 .1111, r	 to' 1 1),    Figure 1. The correct and the error counts of each preposition.References[BR 94] E. Brill and P. Resnik, A Rule-Based Approach to Prepositional Phrase AttachmentDisambiguation. COLING-94.[CB 95] M. Collins and J. Brooks, Prepositional Phrase Attachment through a Backed-OffModel. ACL 3rd Workshop on Very Large Corpora, 1995.[FB 92] W. B. Frakes and R. Baeza-Yates, Information Retrieval - Data Structures &Algorithms, Prentice-Hall, 1992.[FBK 82] M. Ford, J. Bresnan, and R. Kaplan, A Competence Based Theory of SyntacticClosure, in The Mental Representation of Grammatical Relations, MIT Press, 1982.[HR 93] D. Hindle and M. Rooth, Structural Ambiguity and Lexical Relations. ComputationalLinguistics, 19(1):103-120, 1993.[HI 87] G. Hirst, Semantic Interpretation and the Resolution of Ambiguity, CambridgeUniversity Press, 1987. ISBN 0 521 32203 0.[J13 87] K. Jenson and J. Binot, Disambiguating Prepositional Phrase Attachments by UsingOn-Line Dictionary Definitions, Computational Linguistics, 13(3-4): 251-260,1987.[KC 95] S. J. Ker and Jason J.S. Chang. Simultaneous Resolution of Word Alignment andSense Ambiguity - An approach based on thesaurus classes, submitted to PACLIC-10,1995.118[LO 92] Longman, Longman English-Chinese Dictionary of Contemporary English, Publishedby Longman Group (Far East) Ltd., Hong Kong, 1992.[MC 92] Tom McArthur, Longman Lexicon of Contemporary English, Published by LongmanGroup (Far East) Ltd., Hong Kong, 1992.[MA 80] Marcus and P. Mitchel, A Theory of Syntactic Recognition for Natural Language.MIT Press , 1980.[ME 93] J. J. Mei, et al. Tongyici Cilin (A word-tree of symnonyms), Tong Hua Publishing,Taipei, 1993 (traditional Chinese edition of a simplified Chinese edition published in1984).[PU 93] J. Pustejovsky, Sabine Bergler, and Peter Anick, Lexical Semantic Techniques forCorpus Analysis. Computational Linguistics, 19(2):331-358,1993.[RA 94] A. Ratnaparkhi, J. Reynar, and S. Roukos, A Maximum Entropy Model forPrepositional Phrase Attachment. ARPA Workshop on Human Language Technology,1994.[RH 93] P. Resnik and M. A. Hearst. Structural Ambiguity and Conceptual Relations. InProceedings of ACL Workshop on Very Large Corpora, 1993.[UR 95] N. Uramoto, Automatic Learning of Knowledge for Example-Based Disambiguationof Attachment, TMI-95.[WFB 90] G. Whittemore, K. Ferrara, and H. Brunner. Empirical Study of Predicative Powersof Simple Attachment Schemes, ACL-90.119120

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Structural Ambiguity and Conceptual Information Retrieval

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