Automatic Mapping Clinical Notes to Medical - RMIT University

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TIME ACC COVER FAIL RATE % secs % % % n ≤ 40 n > 40 Original 91.9 — 86.92 99.29 0.621 1.961 punctuation + repair 63.5 30.9 86.89 99.58 0.399 0.654 Table 2: Parsing performance on Section 23 with constraints and chart repair. tempts. This allows us to further exploit the tight integration of the supertagger and parser by adding one lexical category at a time until a parse of the sentence is found. Chart repair alone gives an 11% improvement in speed. Constraints improve efficiency by avoiding the construction of sub-derivations that will not be used. They have a significant impact on parsing speed and coverage without reducing the accuracy, provided the constraints are identified with sufficient precision. When both methods are used the speed increases by 30-35%, the failure rate decreases by 40-65%, both for sentences of length 1-40 and 41+, while the accuracy is not decreased. The result is an even faster state-of-the-art widecoverage CCG parser. 12 Acknowledgments We would like to thank the anonymous reviewers for their feedback. This research was funded under Australian Research Council Discovery grants DP0453131 and DP0665973. References Srinivas Bangalore and Aravind Joshi. 1999. Supertagging: An approach to almost parsing. Computational Linguistics, 25(2):237–265. Adam L. Berger, Stephen Della Pietra, and Vincent J. Della Pietra. 1996. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1):39–71. Fabio Ciravegna, Alberto Lavelli, and Giorgio Satta. 1997. Efficient full parsing for Information Extraction. In Proceedings of the Meeting of the Working Groups on Automatic Learning and Natural Language of the Associazione Italiana per l’Intelligenza Artificale (AI*IA), Turin, Italy. Stephen Clark and James R. Curran. 2004. The importance of supertagging for wide-coverage CCG parsing. In 20th International Conference on Computational Linguistics, pages 282–288, Geneva, Switzerland. Stephen Clark and James R. Curran. 2006. Wide-coverage statistical parsing with CCG and log-linear models. (submitted). 8 Stephen Clark. 2002. A supertagger for Combinatory Categorial Grammar. In Proceedings of the TAG+ Workshop, pages 19–24, Venice, Italy. Ronald Cole, Joseph Mariani, Hans Uszkoreit, Annie Zaenen, and Victor Zue. 1997. Survey of the state of the art in human language technology. Cambridge University Press, New York, NY, USA. Jay Earley. 1970. An efficient context-free parsing algorithm. Communications of the ACM, 13(2):94–102. Ralph Grishman. 1997. Information Extraction: Techniques and challenges. In SCIE ’97: International Summer School on Information Extraction, pages 10–27, London, UK. Springer-Verlag. Julia Hockenmaier and Mark Steedman. 2006. CCGbank - a corpus of CCG derivations and dependency structures extracted from the Penn Treebank. (submitted). Dan Klein and Christopher D. Manning. 2003. A* parsing: Fast exact Viterbi parse selection. In Proceedings of Human Language Technology and the North American Chapter of the Association for Computational Linguistics Conference, pages 119–126, Edmond, Canada. Klass Sikkel and Anton Nijholt. 1997. Parsing of Context- Free languages. In Grzegorz Rozenberg and Arto Salomaa, editors, Handbook of Formal Languages, Volume 2: Linear Modelling: Background and Application, pages 61–100. Springer-Verlag, New York. Mark Steedman. 2000. The Syntactic Process. The MIT Press, Cambridge, MA. K. Vijay-Shanker and David J. Weir. 1990. Polynomial time parsing of combinatory categorial grammars. In Proceedings of the 28th Annual Meeting on Association for Computational Linguistics, pages 1–8, Pittsburgh, Pennsylvania. Daniel H. Younger. 1967. Recognition and parsing of contex-free languages in time n 3 . Information and Control, 10(2):189–208.
Improved Default Sense Selection for Word Sense Disambiguation Tobias HAWKER and Matthew HONNIBAL Language Technology Research Group School of Information Technologies University of Sydney {toby,mhonn}@it.usyd.edu.au Abstract small, and most words will have only a few examples. The low inter-annotator agreement exac- Supervised word sense disambiguation erbates this problem, as the already small samples has proven incredibly difficult. Despite are thus also somewhat noisy. These difficulties significant effort, there has been little suc- mean that different sense frequency heuristics can cess at using contextual features to accu- significantly change the performance of a ‘baserately assign the sense of a word. Instead, line’ system. In this paper we discuss several such few systems are able to outperform the de- heuristics, and find that most outperform the comfault sense baseline of selecting the highmonly used first-sense strategy, one by as much as est ranked WordNet sense. In this pa- 1.3%. per, we suggest that the situation is even worse than it might first appear: the highest ranked WordNet sense is not even the best default sense classifier. We evaluate several default sense heuristics, using supersenses and SemCor frequencies to achieve significant improvements on the WordNet ranking strategy. The sense ranks in WordNet are derived from semantic concordance texts used in the construction of the database. Most senses have explicit counts listed in the database, although sometimes the counts will be reported as 0. In these cases, the senses are presumably ranked by the lexicographer’s intuition. Usually these counts are higher than the frequency of the sense in the SemCor 1 Introduction sense-tagged corpus (Miller et al., 1993), although not always. This introduces the first alternative Word sense disambiguation is the task of select- heuristic: using SemCor frequencies where availing the sense of a word intended in a usage conable, and using the WordNet sense ranking when text. This task has proven incredibly difficult: there are no examples of the word in SemCor. We in the SENSEVAL 3 all words task, only five of find that this heuristic performs significantly better the entered systems were able to use the contex- than the first-sense strategy. tual information to select word senses more ac- Increasing attention is also being paid to coarse curately than simply selecting the sense listed as grained word senses, as it is becoming obvious most likely in WordNet (Fellbaum, 1998). Suc- that WordNet senses are too fine grained (Hovy cessful WSD systems mostly fall back to the first- et al., 2006). Kohomban and Lee (2005) explore sense strategy unless the system was very confi- finding the most general hypernym of the sense dent in over-ruling it (Hoste et al., 2001). being used, as a coarser grained WSD task. Sim- Deciding which sense of a word is most likely, ilarly, Ciaramita and Altun (2006) presents a sys- irrespective of its context, is therefore a crucial tem that uses sequence tagging to assign ‘super- task for word sense disambiguation. The decision senses’ — lexical file numbers — to words. Both is complicated by the high cost (Chklovski and of these systems compare their performance to a Mihalcea, 2002) and low inter-annotator agree- baseline of selecting the coarse grained parent of ment (Snyder and Palmer, 2004) of sense-tagged the first-ranked fine grained sense. Ciaramita and corpora. The high cost means that the corpora are Altun also use this baseline as a feature in their Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), pages 9–15. 9
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References Aronson, A. R. (2001). E
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technique to most question types, i
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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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