Automatic Mapping Clinical Notes to Medical - RMIT University

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Analysis and Review. Springer-Verlag New York, Inc. Xin Li and Dan Roth. 2002. Learning question classifiers. In Proceedings of the 19th international conference on Computational linguistics, pages 556– 562, Taipei, Taiwan. Association for Computational Linguistics. Diego Molla and Menno van Zaanen. 2006. Answerfinder at TREC 2005. In The Fourteenth Text REtrieval Conference (TREC 2005), Gaithersburg, Maryland. National Institute of Standards and Technology. Christof Monz. 2004. Minimal span weighting retrieval for question answering. In Proceedings of the SIGIR-2004 Workshop on Information Retrieval For Question Answering (IR4QA), Sheffield, UK, July. Jori Mur. 2004. Off-line answer extraction for dutch qa. In Computational Linguistics in the Netherlands 2004: Selected papers from the fifteenth CLIN meeting, pages 161–171, Leiden University. Vanessa Murdock and W. Bruce Croft. 2004. Simple translation models for sentence retrieval in factoid question answering. In Proceedings of the SIGIR- 2004 Workshop on Information Retrieval For Question Answering (IR4QA), Sheffield, UK, July. Ian Roberts and Robert J. Gaizauskas. 2004. Evaluating passage retrieval approaches for question answering. In Sharon McDonald and John Tait, editors, ECIR, volume 2997 of Lecture Notes in Computer Science, pages 72–84. Springer. Stephen E. Robertson, Steve Walker, Micheline Hancock-Beaulieu, Aarron Gull, and Marianna Lau. 1992. Okapi at TREC. In Text REtrieval Conference, pages 21–30. I. Ruthven and M. Lalmas. 2003. A survey on the use of relevance feedback for information access systems. Knowledge Engineering Review, 18(2):95– 145. Stefanie Tellex, Boris Katz, Jimmy Lin, Aaron Fernandes, and Gregory Marton. 2003. Quantitative evaluation of passage retrieval algorithms for question answering. In SIGIR ’03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 41–47, Toronto, Canada. ACM Press. Jorg Tiedemann. 2005. Optimizing information retrieval in question answering using syntactic annotation. In Proceedings of RANLP 2005, pages 540– 546, Borovets, Bulgaria. Ellen M. Voorhees and Dawn M. Tice. 2000. Building a question answering test collection. In SI- GIR ’00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pages 200–207, Athens, Greece. ACM Press. 88 Ellen M. Voorhees. 2005. Overview of the TREC 2004 question answering track. In Text REtrieval Conference. Kieran White and Richard F. E. Sutcliffe. 2004. Seeking an upper bound to sentence level retrieval in question answering. In Proceedings of the SIGIR- 2004 Workshop on Information Retrieval For Question Answering (IR4QA), Sheffield, UK, July. Menno Van Zaanen, Luiz Augusto Pizzato, and Diego Molla. 2005. Classifying sentences using induced structure. In Proceedings of the Twelfth Symposium on String Processing and Information Retrieval (SPIRE-2005)., Buenos Aires, Argentina, November.
Evidence for Gradient Salience: What Happens with Competing Non-salient Referents during Pronoun Resolution? Abstract The necessity of a gradient approach to salience ranking of referents introduced in a discourse is evaluated by looking at (unbound) pronoun resolution preferences when there are competing non-salient referents. The study uses a sentencecompletion technique in which participants had to resolve pronouns (“John sprayed the paint on the wall and then it ...”). Results suggest that a gradient salience model is necessary. Syntactic and semantic prominence effects on pronoun resolution were also compared with results showing that semantic prominence (i.e., agent > patient) determined the salience ranking of competing referents. 1 Introduction A pervasive theme in theories of discourse coherence is the concept of salience. It has proven to be a useful means of explaining how particular entities seem to receive some preferential treatment in both the production and perception of a discourse. For instance, it has long been observed that entities realized in certain structural positions (e.g., grammatical subject or first-mention) are preferred entities for topic continuation (Givón, 1983)—that is, they are preferentially referred to in the subsequent utterance. Similarly, pronominal reference to entities realized in certain structural positions (again, subject position, for example) is preferred to reference by repeated name (Gordon et al., 1993). In order to account for these observations, it has often been theorized that in the speaker’s and hearer’s mental representation of the discourse, these entities are salient (similar terms include focused or given). Ralph L. Rose Faculty of International Communication Gunma Prefectural Women’s University Gunma, Japan rose@gpwu.ac.jp To illustrate this line of thinking, consider (1). The pronoun in the second clause is preferentially interpreted as referring to LUKE rather than MAX. This has been observed in numerous psycholinguistic investigations (cf., Hudson- D’Zmura and Tanenhaus (1997); Mathews and Chodorow (1988); inter alia). In a simple saliencebased account, it is hypothesized that LUKE is a salient entity after the first clause and that reference to salient entities should be pronominal. (1) Lukei hit Maxj and then he i/#j ran home. While many studies have investigated differences between pronominal reference to salient and non-salient entities, I have found no studies that have looked explicitly at what happens when a salient entity is not compatible with the pronoun, but more than one non-salient entity is. This is one of the main themes of the present study. Putting it as a question, what happens when there is competition among non-salient entities for pronoun interpretation? The answer to this question has some wider implications for how salience is to be understood. In particular, the answer to this question leads to conclusions about whether theoretical models require a gradient model of salience ranking or whether a categorical model is sufficient. In the following background section I will discuss this primary question further and introduce two related questions which must also be addressed. This will be followed by description of the experiment performed in this study. Briefly, results of the experiment are consistent with a gradient model of salience ranking. Implications of these findings are discussed in the final section. Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), pages 89–96. 89
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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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The cat ate the hat that I made NP/
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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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Page 82 and 83: Figure 1: Augmented SCT Lexicon Tok
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Page 86 and 87: References Aronson, A. R. (2001). E
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Page 96 and 97: 2 Background 2.1 Pronoun Reference
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Given the lack of research in VSD u
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ased features: Type 1 Original sibl
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Preposition of the adjunct semantic
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Algorithm 1 The algorithm of combin
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References Timothy Baldwin and Fran
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dependencies in German with respect
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wij moeten dit probleem aanpakken w
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a brevity penalty of BLEU n = 1 n =
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plicitly matching the syntax of the
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Probabilities improve stress-predic
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Towards Cognitive Optimisation of a
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Natural Language Processing and XML
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Extracting Patient Clinical Profile
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