Automatic Mapping Clinical Notes to Medical - RMIT University

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acy. The difference in correctly assigning senses between the baseline and best-performing systems is statistically significant (paired t-test, p < 0.01). 5.1 Token Types Table 2 shows the behaviour of the best performing system for the tokens it covers. The hapax legomena row aggregates scores for content words in this category that occur only once in the test data. The Newly Wrong and Newly Correct columns refer to the number of instances where the change of default sense has changed from correct to incorrect or vice versa, as compared to the first-sense baseline. The Net Gain column indicates the overall contribution from this token type to the performance of the system. The Not in Senseval row indicates the tokens where the default sense would be changed, but did not occur in the SENSEVAL test data. Including these tokens in the count allows an accurate comparison of the total coverage of the changed tokens for both corpora. The table shows that there are both gains and losses for the strategy when compared to the baseline, as should be expected for a classifier limited to assigning only one sense per term. However, the net effect is significantly positive. The table also shows that this positive performance is not simply down to one or two frequent words the heuristic happens to make the right decision on. Almost one third of the newly correct tokens come from decisions made on words that occur only once in the test data. This supports the suggestion above that the heuristic is identifying a range of volatile terms. 6 Conclusions We have evaluated several heuristics for assigning a default WordNet sense to terms. Our results consistently showed that heuristics which were more sensitive to frequencies in the SemCor corpus outperformed heuristics exclusively based on Word- Net sense rankings — suggesting that the stated baselines for SENSEVAL 3 are actually lower than they should be. This is somewhat alarming, considering that systems struggle to make even marginal improvements over the first sense baseline. Since the SemCor data is used to train the supervised systems, the most frequent sense can be inferred — allowing a system to compare 14 favourably with the baseline even if it does not actually gain anything significant from the context of the word. We have shown that a more nuanced default sense heuristic can achieve some performance gains over simple frequency heuristics, as sense tagged corpora are not large enough to produce entirely reliable fine-grained sense frequencies. By using the frequency of coarse-grained senses, in the form of the lexicographer file number, we are able to identify instances where these frequencies are particularly suspect, thus making slightly more accurate default sense predictions. We have also shown that a system limited to selecting default senses still has an upper bound far beyond current state of the art — even excluding rare words. This is consistent with the results reported by McCarthy et al. (2004), who show that a classifier limited to selecting one sense per word was able to perform well if the sense was chosen intelligently. Their method, which relies on distributional similarity, might be adopted as a supersense selection heuristic. This might prove useful, as we have shown that using a different method to choose a supersense can be used to change the default prediction in cases where the simple baseline system performs poorly. 7 Acknowledgements We would like to thank James Curran, James Gorman and Jon Patrick from the University of Sydney for their invaluable insights. References Timothy Chklovski and Rada Mihalcea. 2002. Building a sense tagged corpus with open mind word expert. In Proceedings of the Workshop on “Word Sense Disambiguation: Recent Successes and Future Directions”, pages 116–122. Massimiliano Ciaramita and Yasemin Altun. 2006. Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger. In Proceedings of EMNLP 2006, pages 594–602. Massimiliano Ciaramita and Mark Johnson. 2003. Supersense taggin of unknown nouns in wordnet. In Proceedings of EMNLP 2003. Bart Decadt, Véronique Hoste, Walter Daelemans, and Antal van den Bosch. 2004. GAMBL, genetic algorithm optimization of memory-based
WSD. In Proceedings of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (SENSEVAL), pages 108–112. Barcelona, Spain. Christiane Fellbaum, editor. 1998. Wordnet: An Electronic Lexical Database. MIT Press. Véronique Hoste, Anne Kool, and Walter Daelemans. 2001. Classifier optimization and combination in the english all words task. In Proceedings of the SENSEVAL-2 workshop, pages 84–86. Eduard Hovy, Mitchell Marcus, Martha Palmer, Lance Ramshaw, and Ralph Weischedel. 2006. Ontonotes: the 90% solution. In Proceedings of HLT-NAACL 2006, New York, pages 57–60. Upali S. Kohomban and Wee Sun Lee. 2005. Learning semantic classes for word sense disambiguation. In Proceedings of the 43rd Annual Meeting of the ACL, Ann Arbor, pages 34–41. D. McCarthy, R. Koeling, J. Weeds, and J. Carroll. 2004. Using automatically acquired predominant senses for word sense disambiguation. In Proceedings of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (SENSEVAL), pages 151–154. Barcelona, Spain. G. A. Miller, C. Leacock, T. Randee, and R. Bunker. 1993. A semantic concordance. In Proceedings of the 3 DARPA Workshop on Human Language Technology, pages 303–308. Benjamin Snyder and Martha Palmer. 2004. The english all-words task. In Proceedings of the SENSEVAL-3 Workshop, Barcelona, pages 41– 43. 15
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