Automatic Mapping Clinical Notes to Medical - RMIT University

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L ATEX typesetting information into a useable format. Unlike existing work we have rendered the mathematics in Unicode text rather than just removing it, which is important for further analysis of the data. The resulting corpus is larger than existing resources, such as muc, but has been annotated with a much more detailed set of over 40 named entity classes. Finally, we have demonstrated that high accuracy named entity recognisers can be trained using the initial release of this corpus, and shown how the tagger can be used to iteratively identify potential tagging errors. The quality of the results should only improve as the corpus size and quality is increased. 11 Acknowledgments We would like to thank the arXiv administrators for giving us access to the astrop-ph archive. This research has made use of nasa’s Astrophysics Data System Bibliographic Services. This research has made use of the nasa/ipac Extragalactic Database (ned) operated by the Jet Propulsion Laboratory, California Institute of Technology. This research was funded under a University of Sydney Research and Development Grant and ARC Discovery grants DP0453131 and DP0665973. References ADS. 2005. Astronomical Data Service. http://www. adsabs.harvard.edu/. arXiv. 2005. arXiv.org archive. http://arxiv.org. M. Becker, B. Hachey, B. Alex, and C. Grover. 2005. Optimising selective sampling for bootstrapping named entity recognition. In Proceedings of the ICML Workshop on Learning with Multiple Views, pages 5–11, Bonn, Germany. J. Cohen. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20:37–46. M. Collins. 2002. Ranking algorithms for named-entity extraction: Boosting and the voted perceptron. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 489– 496, Philadephia, PA USA. J.R. Curran and S. Clark. 2003. Language independent ner using a maximum entropy tagger. In Proceedings of the 7th Conference on Natural Language Learning (CoNLL), pages 164–167, Edmonton, Canada. 64 M. Dickinson and W.D. Meurers. 2003. Detecting errors in part-of-speech annotation. In Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics, pages 107–114, Budapest, Hungary. P. Ginsparg. 2001. Creating a global knowledge network. In UNESCO Expert Conference on Electronic Publishing in Science, Paris, France. J. Grimm. 2003. Tralics, a L ATEX to XML translator. TUGboat, 24(3):377 – 388. B. Hachey, B. Alex, and M. Becker. 2005. Investigating the effects of selective sampling on the annotation task. In Proceedings of the 9th Conference on Natural Language Learning (CoNLL), pages 144– 151, Ann Arbor, MI USA. R. J. Hanisch and P. J. Quinn. 2005. The IVOA. http://www.ivoa.net/pub/info/. S. Harabagiu, D. Moldovan, M. Pa¸sca, R. Mihalcea, M. Surdeanu, R. Bunescu, R. Gîrju, V. Rus, and P. Morărescu. 2000. Falcon: Boosting knowledge for answer engines. In Proceedings of TREC-9. L. Hirschman and R. Gaizauskas. 2001. Natural language question answering: The view from here. Journal of Natural Language Engineering, 7(4):275– 300. L. Hirschman, J.C. Park, J. Tsujii, L. Wong, and C.H. Wu. 2002. Accomplishments and challenges in literature data mining for biology. Bioinformatics, 18(12):1553–1561. J.-D. Kim, T. Ohta, Y. Tateisi, and J. Tsujii. 2003. GENIA corpus - a semantically annotated corpus for bio-textmining. Bioinformatics, 19(s1):i180–i182. M.-C. Lortet, S. Borde, and F. Ochsenbein. 1994. Second Reference Dictionary of the Nomenclature of Celestial Objects. A&AS, 107:193–218, October. M.P. Marcus, B. Santorini, and M.A. Marcinkiewicz. 1994. Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, 19(2):313–330. A. P. Martinez, S. Derriere, N. Gray, R. Mann, J. Mc- Dowell, T. McGlynn, Ochsenbein F., P. Osuna, G. Rixon, and R. Williams. 2005. The UCD1+ controlled vocabulary Version 1.02. A. Ratnaparkhi. 1996. A maximum entropy part-ofspeech tagger. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 133–142, Philadelphia, PA USA. J.C. Reynar and A. Ratnaparkhi. 1997. A maximum entropy approach to identifying sentence boundaries. In Proceedings of the 5th Conference on Applied Natural Language Processing, pages 16–19, Washington DC, USA. Daniel Trinkle. 2002. Detex. http://www.cs.purdue. edu/homes/trinkle/detex/. Unicode Consortium. 2005. The Unicode Standard. Addison-Wesley, 4th edition.
Die Morphologie (f) : Targeted Lexical Acquisition for Languages other than English Jeremy Nicholson†, Timothy Baldwin†, and Phil Blunsom‡ †‡ Department of Computer Science and Software Engineering University of Melbourne, VIC 3010, Australia and † NICTA Victoria Research Laboratories University of Melbourne, VIC 3010, Australia {jeremymn,tim,pcbl}@csse.unimelb.edu.au Abstract We examine standard deep lexical acquisition features in automatically predicting the gender of noun types and tokens by bootstrapping from a small annotated corpus. Using a knowledge-poor approach to simulate prediction in unseen languages, we observe results comparable to morphological analysers trained specifically on our target languages of German and French. These results describe further scope in analysing other properties in languages displaying a more challenging morphosyntax, in order to create language resources in a language-independent manner. 1 Introduction As a result of incremental annotation efforts and advances in algorithm design and statistical modelling, deep language resources (DLRs, i.e. language resources with a high level of linguistic sophistication) are increasingly being applied to mainstream NLP applications. Examples include analysis of semantic similarity through ontologies such as WordNet (Fellbaum, 1998) or VerbOcean (Chklovski and Pantel, 2004), parsing with precision grammars such as the DELPH-IN (Oepen et al., 2002) or PARGRAM (Butt et al., 2002) grammars, and modelling with richly annotated treebanks such as PropBank (Palmer et al., 2005) or CCGbank (Hockenmaier and Steedman, 2002). Unfortunately, the increasing complexity of these language resources and the desire for broad coverage has meant that their traditionally manual mode of creation, development and maintenance has become infeasibly labour-intensive. As a consequence, deep lexical acquisition (DLA) has been proposed as a means of automatically learning deep linguistic representations to expand the coverage of DLRs (Baldwin, 2005). DLA research can be divided into two main categories: targeted DLA, in which lexemes are classified according to a given lexical property (e.g. noun countability, or subcategorisation properties); and generalised DLA, in which lexemes are classified according to the full range of lexical properties captured in a given DLR (e.g. the full range of lexical relations in a lexical ontology, or the system of lexical types in an HPSG). As we attest in Section 2, most work in deep lexical acquisition has focussed on the English language. This can be explained in part by the ready availability of targeted language resources like the ones mentioned above, as well as secondary resources — such as corpora, part-ofspeech taggers, chunkers, and so on — with which to aid prediction of the lexical property in question. One obvious question, therefore, is whether the techniques used to perform lexical acquisition in English generalise readily to other languages, where subtle but important differences in morphosyntax might obfuscate the surface cues used for prediction. In this work, we will examine the targeted prediction of the gender of noun types and tokens in context, for both German and French. As an example, the following phrases display adjectival gender inflection in two of the three languages below. the open window das offene Fenster la fenêtre ouverte window has no gender in English, Fenster is neuter in German and fenêtre is feminine in French. So Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), pages 65–72. 65
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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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model. We explore different estimat
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S.S. Source Sense Source S.S. Acc.
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algorithms, and report the performa
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2.3 Coverage of the Human Data Out
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and minimal subsets of the data. Of
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output. Finally, and related to the
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Features Acc. C1-prec. C1-recall C1
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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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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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