Automatic Mapping Clinical Notes to Medical - RMIT University

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References Timothy Baldwin and Francis Bond. 2003. Learning the countability of english nouns from corpus data. In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pages 73–80, Sapporo, Japan. Adam L. Berger, Vincent J. Della Pietra, and Stephen A. Della Pietra. 1996. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1):39–71. Eugene Charniak. 2000. Maximum-entropy-inspired parser. In Proceedings of the First Conference on North American Chapter of the ACL, pages 132– 139, San Francisco, USA. Trevor Cohn, Andrew Smith, and Miles Osborne. 2005. Scaling conditional random fields using errorcorrecting codes. In Proceedings of the 43rd Annual Meeting of the ACL, pages 10–17, Ann Arbor, USA. Christiane Fellbaum, editor. 1998. WordNet: An Electronic Lexical Database. MIT Press, Cambridge, USA. Jess Gimnez and Llus Mrquez. 2004. SVMTool: A general POS tagger generator based on support vector machines. In Proceedings of the 4th International Conference on Language Resources and Evaluation, pages 43–46, Lisbon, Portugal. Kadri Hacioglu, Sameer Pradhan, Wayne Ward, James H. Martin, and Daniel Jurafsky. 2004. Semantic role labeling by tagging syntactic chunks. In Proc. of the 8th Conference on Natural Language Learning (CoNLL-2004), pages 110–113, Boston, USA. Anna Korhonen and Judita Preiss. 2003. Improving subcategorization acquisition using word sense disambiguation. In Proceedings of the 41st Annual Meeting of the ACL, pages 48–55, Sapporo, Japan. Mitchell P. Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993. Building a large annotated corpus of English: the Penn treebank. Computational Linguistics, 19(2):313–330. Diana McCarthy and John Carroll. 2003. Disambiguating nouns, verbs, and adjectives using automatically acquired selectional preferences. Computational Linguistics, 29(4):639–654, December. Philip Resnik. 1997. Selectional preference and sense disambiguation. In Proceedings of ACL SIGLEX Workshop on Tagging Text with Lexical Semantics, Why, What and How?, pages 52–57, Washington, USA. Erik Tjong Kim Sang and Jorn Veenstra. 1999. Representing text chunks. In Proceedings of EACL’99: Ninth Conference of the European Chapter of the ACL, pages 173–179, Bergen, Norway. David Yarowsky, 2000. Handbook of Natural Language Processing, chapter 26. Marcel Dekker. 146
This Phrase-Based SMT System is Out of Order: Generalised Word Reordering in Machine Translation Simon Zwarts Centre for Language Technology Macquarie University Sydney, Australia szwarts@ics.mq.edu.au Abstract Many natural language processes have some degree of preprocessing of data: tokenisation, stemming and so on. In the domain of Statistical Machine Translation it has been shown that word reordering as a preprocessing step can help the translation process. Recently, hand-written rules for reordering in German–English translation have shown good results, but this is clearly a labour-intensive and language pair-specific approach. Two possible sources of the observed improvement are that (1) the reordering explicitly matches the syntax of the source language more closely to that of the target language, or that (2) it fits the data better to the mechanisms of phrasal SMT; but it is not clear which. In this paper, we apply a general principle based on dependency distance minimisation to produce reorderings. Our languageindependent approach achieves half of the improvement of a reimplementation of the handcrafted approach, and suggests that reason (2) is a possible explanation for why that reordering approach works. Help you I can, yes. Jedi Master Yoda 1 Introduction Preprocessing is an essential step in Natural Language applications. Reordering of words on a sentence level as a more extensive step for preprocessing has succeeded in improving results in Statistical Machine Translation (SMT). Here, both in the training and in the decoding phase, sentences in the source language are reordered before being processed. This reordering can be done based on rules over word alignment learnt statistically; (Costa-Juss and Donollosa, 2006), for example, describe such a system. In this work an improvement in overall translation quality in a Spanish-English MT Mark Dras Centre for Language Technology Macquarie University Sydney, Australia madras@ics.mq.edu.au system was achieved by using statistical word classes and a word-based distortion model to reorder words in the source language. Reordering here is purely a statistical process and no syntactical knowledge of the language is used. Xia and McCord (2004) do use syntactical knowledge; they use pattern learning in their reordering system. In their work in the training phase they parse and align sentences and derive reordering patterns. From the English-French Canadian Hansard they extract 56,000 different transformations for translation. In the decoding phase they use these transformations on the source language. The main focus then is monotonic decoding (that is, decoding while roughly keeping the same order in the target language as in the source language — reordering done within phrases, for example, is an exception). Syntactically motivated rules are also used in reordering models. In Collins et al. (2005) six handwritten rules for reordering source sentences are defined. These rules operate on the output of an automatic parser. The reordering rules however are language-pair (German-English) specific and hand-written. We want to extend this idea of word reordering as preprocessing by investigating whether we can find a general underlying principle for reordering, to avoid either thousands of patterns, or arguably arbitrary hand-written rules to do this. To do this, we note that a common characteristic of the Collins et al. (2005) rules is that they reduce the distances of a certain class of long-distance Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), pages 147–154. 147
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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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References Aronson, A. R. (2001). E
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