Automatic Mapping Clinical Notes to Medical - RMIT University

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Features Acc. C1-prec. C1-recall C1-Fmeas. C2-prec C2-recall C2-Fmeas. Best Features 75.63 0.70 0.46 0.55 0.77 0.90 0.83 All Features 71.00 0.55 0.71 0.61 0.83 0.72 0.77 Table 4: Classification performance of the best feature vector found and the feature vector including weighted dependency overlap. C1 denotes False Paraphrase pairs, C2 denotes True Paraphrase pairs. feedback. References Colin Bannard and Chris Callison-Burch. 2005. Paraphrasing with bilingual parallel corpora. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), Ann Arbor, Michigan. Regina Barzilay and Lillian Lee. 2003. Learning to paraphrase: An unsupervised approach using multiple-sequence alignment. In HLT-NAACL 2003: Main Proceedings. Michael John Collins. 1996. A new statistical parser based on bigram lexical dependencies. In Proceedings of the Thirty-Fourth Annual Meeting of the Association for Computational Linguistics, San Francisco. Courtney Corley and Rada Mihalcea. 2005. Measuring the semantic similarity of texts. In Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, Michigan. Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005. The pascal recognising textual entailment challenge. In Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment. Hal Daumé and Daniel Marcu. 2005. Bayesian multidocument summarization at mse. In Proceedings of the Workshop on Multilingual Summarization Evaluation (MSE), Ann Arbor, MI. Bill Dolan, Chris Quirk, and Chris Brockett. 2004. Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources. In Proceedings of Coling 2004, Geneva, Switzerland. Mark Dras. 1999. Tree Adjoining Grammar and the Reluctant Paraphrasing of Text. Ph.D. thesis, Macquarie University, Australia. Andrew Finch, Young Sook Hwang, and Eiichiro Sumita. 2005. Using machine translation evaluation techniques to determine sentence-level semantic equivalence. In Proceedings of The 3rd International Workshop on Paraphrasing (IWP2005). Eduard Hovy, Chin-Yew Lin, and Liang Zhou. 2005. Evaluating duc 2005 using basic elements. In Proceedings of Document Understanding Conference (DUC 2005), Vancouver, B.C. Canada. 136 Milen Kouylekov and Bernardo Magnini. 2005. Recognizing textual entailment with tree edit distance algorithms. In Proceedings of the PASCAL Challenges Workshop: Recognising Textual Entailment Challenge. Erwin Marsi and Emiel Krahmer. 2005. Explorations in sentence fusion. In The Proceedings of the European Workshop on Natural Language Generation 2005, Aberdeen, Scotland. Diego Mollá. 2003. Towards semantic-based overlap measures for question answering. In Proceedings of the Australasian Language Technology Workshop (ALTW 2003), Melbourne. Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, July. V. Punyakanok, D. Roth, and W. Yih. 2004. Mapping dependencies trees: An application to question answering. In Proceedings of AI & Math. Long Qiu, Min-Yen Kan, and Tat-Seng Chua. 2006. Paraphrase recognition via dissimilarity significance classification. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, Australia. Stephen Wan, Mark Dras, Robert Dale, and Cécile Paris. 2005. Towards statistical paraphrase generation: preliminary evaluations of grammaticality. In Proceedings of The 3rd International Workshop on Paraphrasing (IWP2005), Jeju Island, South Korea. Ian H. Witten and Eibe Frank. 2000. Data mining: practical machine learning tools and techniques with Java implementations. San Francisco, CA, USA. Etienne Denoual Yves Lepage. 2005. Automatic generation of paraphrases to be used as translation references in objective evaluation measures of machine translation. In Proceedings of The 3rd International Workshop on Paraphrasing (IWP 2005). Yitao Zhang and Jon Patrick. 2005. Paraphrase identification by text canonicalization. In Proceedings of the Australasian Language Technology Workshop 2005, Sydney, Australia. K. Zhang and D. Shasha. 1989. Simple fast algorithms for the editing distance between trees and related problems. SIAM Journal of Computing, 18(6).
Verb Sense Disambiguation Using Selectional Preferences Extracted with a State-of-the-art Semantic Role Labeler Patrick Ye and Timothy Baldwin Dept. of Computer Science and Software Engineering University of Melbourne Victoria 3010 Australia Abstract This paper investigates whether multisemantic-role (MSR) based selectional preferences can be used to improve the performance of supervised verb sense disambiguation. Unlike conventional selectional preferences which are extracted from parse trees based on hand-crafted rules, and only include the direct subject or the direct object of the verbs, the MSR based selectional preferences to be presented in this paper are extracted from the output of a state-of-the-art semantic role labeler and incorporate a much richer set of semantic roles. The performance of the MSR based selectional preferences is evaluated on two distinct datasets: the verbs from the lexical sample task of SENSEVAL-2, and the verbs from a movie script corpus. We show that the MSR based features can indeed improve the performance of verb sense disambiguation. 1 Introduction Verb sense disambiguation (VSD) is the task of examining verbs in a given context and specifying exactly which sense of each verb is the most appropriate in that context. VSD is a subtask of word sense disambiguation (WSD). Given a verb sense inventory and a set of verb senses, VSD is essentially a classification task (Yarowsky, 2000). VSD has not received much attention in the literature of WSD until recently. Most of WSD systems disambiguate verbs in the same way as nouns using mostly collocation based features, and this has led to rather poor VSD performance on corpora such as SENSEVAL-2. One useful but often {jingy,tim}@csse.unimelb.edu.au ignored source of disambiguation information for verbs is the patterns of verb-argument structures. In this paper, we will attempt to capture these patterns through the use of selectional preferences. In general, selectional preferences describe the phenomenon that predicating words such as verbs and adjectives tend to favour a small number of noun classes for each of their arguments. For example, the verb eat (“take in solid food”) tends to select nouns from to the ANIMATED THING class as its EATER role, and nouns from the EDIBLE class as its EATEE role. However, it is possible to extend the concept of selectional preferences to include nouns which function as adjuncts to predicating words. For example, the verb hit in the sentence I hit him with my fists stands for “deal a blow to, either with the hand or with an instrument”, but in the sentence I hit him with a car, it stands for “to collide with”, with the only difference between the two instances of hit being their manner modifiers. Intuitively, the inclusion of verb adjuncts can enrich the semantic roles (SRs) and provide additional disambiguation information for verbs. Therefore, in the rest of this paper, the concept of “semantic role” will include both the arguments and adjuncts of verbs. All the selectional preference based WSD systems to date have only used the subject and direct object of verbs as semantic roles, extracting the necessary argument structure via hand-crafted heuristics (Resnik, 1997; McCarthy and Carroll, 2003, inter alia). As a result, it is difficult to extend the selectional preferences to anything else. However, with recent progress in Semantic Role Labelling (SRL) technology, it is now possible to obtain additional semantic roles such as the indirect object of ditransitive verbs and the locational, temporal and manner adjuncts. Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), pages 137–146. 137
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Proceeding of the Australasian Lang
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Program Chairs: Committees Lawrence
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Towards the Evaluation of Referring
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Figure 2: Cells affected by adding
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were used because the accuracy for
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TIME ACC COVER FAIL RATE % secs % %
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model. We explore different estimat
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S.S. Source Sense Source S.S. Acc.
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acy. The difference in correctly as
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Experiments with Sentence Classific
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datasets with skewed distribution t
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words produced by the feature selec
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Sentence Class NB DT SVM Apology 1.
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Computational Semantics in the Natu
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S[sem = ] -> NP[sem=?subj] VP[sem=?
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Any string which cannot be decompos
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satisfy(Formula,model(D,F),G,pos):n
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Classifying Speech Acts using Verba
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The principles are dichotomous —
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ance. Several additional example ut
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our results. In classifying only th
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Word Relatives in Context for Word
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create a collection of training exa
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Query Sense The nave was rebuilt in
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Algorithm Avg S2LS Avg S3LS Avg S2L
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B. Snyder and M. Palmer. 2004. The
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it is even used as a black box that
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ecognition in QA, so we will not us
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BPER ILOC IPER BLOC BLOC BDATE BLOC
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of noise introduced by the addition
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in English, neither the determiner
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Feature type Positions/description
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Miriam Butt, Helge Dyvik, Tracy Hol
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ently mostly solved by employing hu
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Figure 1: Augmented SCT Lexicon Tok
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medical terms can be composed by ad
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References Aronson, A. R. (2001). E
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technique to most question types, i
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User Question Analyser QA System Se
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