Automatic Mapping Clinical Notes to Medical - RMIT University

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of noise introduced by the additional entities does not affect negatively the process of extracting the answer. 6 Summary and Conclusion In this paper we have focused on the impact of introducing multiple labels with the aim to increase recall in a NER for the task of question answering. In our experiments we have tested the impact of the ANNIE system, and two variations of AFNER, our custom-built system that can be tuned to produce either single labels or multiple labels. The experiments confirm the hypothesis that allowing multiple labelling in order to increase recall of named entities benefits the task of QA. In other words, if the NER has several candidate labels for a string (or a substring of it), it pays off to output the most plausible alternatives. This way the QA system has a better chance to find the answer. The noise introduced by returning more (possibly wrong) entities is offset by the increase of recall. Further work includes the evaluation of the impact of multi-label NE recognition on higherperformance QA systems. In particular we plan to test various versions of the complete AnswerFinder system (not just the baseline setting) with each of the NERs. In addition, we plan to retrain AFNER using more data and more relevant data and explore the impact of the single and multiple methods on the resulting higher-performance NER. Acknowledgements This work is supported by the Australian Research Council under the ARC Discovery grant DP0450750. References [Armour et al.2005] Quintin Armour, Nathalie Japkowicz, and Stan Matwin. 2005. The role of named entities in text classification. In Proceedings CLiNE 2005, Gatineau, Canada. [Carroll et al.1998] John Carroll, Ted Briscoe, and Antonio Sanfilippo. 1998. Parser evaluation: a survey and a new proposal. In Proc. LREC98. [Chieu and Ng2002] Haoi Leong Chieu and Hwee Tou Ng. 2002. Named entity recognition: A maximum entropy approach using global information. In Proceedings COLING 2002. [Gaizauskas et al.1996] Robert Gaizauskas, Hamish Cunningham, Yorick Wilks, Peter Rodgers, and 56 Kevin Humphreys. 1996. GATE: an environment to support research and development in natural language engineering. In Proceedings of the 8th IEEE International Conference on Tools with Artificial Intelligence, Toulouse, France. [Hirschman et al.1999] Lynette Hirschman, Marc Light, Eric Breck, and John D. Burger. 1999. Deep Read: A reading comprehension system. In Proc. ACL’99. University of Maryland. [Humphreys et al.2000] Kevin Humphreys, George Demetriou, and Robert Gaizauskas. 2000. Two applications of information extraction to biological science journal articles: Enzyme interactions and protein structures. In Proceedings of the Pacific Symposium on Biocomputing’ 00 (PSB’00), pages 502–513. Honolulu, Hawaii. [Li and Roth2002] Xin Li and Dan Roth. 2002. Learning question classifiers. Proc. COLING 02. [Mollá and Gardiner2004] Diego Mollá and Mary Gardiner. 2004. Answerfinder - question answering by combining lexical, syntactic and semantic information. In Ash Asudeh, Cécile Paris, and Stephen Wan, editors, Proc. ALTW 2004, pages 9–16, Sydney, Australia. Macquarie University. [Mollá and van Zaanen2005] Diego Mollá and Menno van Zaanen. 2005. Learning of graph rules for question answering. In Tim Baldwin and Menno van Zaanen, editors, Proc. ALTW 2005. ALTA. [Mollá and van Zaanen2006] Diego Mollá and Menno van Zaanen. 2006. Answerfinder at TREC 2005. In Ellen M. Voorhees and Lori P. Buckland, editors, Proc. TREC 2005. NIST. [Mollá2006] Diego Mollá. 2006. Learning of graph-based question answering rules. In Proc. HLT/NAACL 2006 Workshop on Graph Algorithms for Natural Language Processing, pages 37–44. [Noguera et al.2005] Elisa Noguera, Antonio Toral, Fernando Llopis, and Rafael Muñoz. 2005. Reducing question answering input data using named entity recognition. In Proceedings of the 8th International Conference on Text, Speech & Dialogue, pages 428–434. [Sundheim1995] Beth M. Sundheim. 1995. Overview of results of the MUC-6 evaluation. In Proc. Sixth Message Understanding Conference MUC-6. Morgan Kaufmann Publishers, Inc. [Tapanainen and Järvinen1997] Pasi Tapanainen and Timo Järvinen. 1997. A non-projective dependency parser. In Proc. ANLP-97. ACL. [Voorhees1999] Ellen M. Voorhees. 1999. The TREC- 8 question answering track report. In Ellen M. Voorhees and Donna K. Harman, editors, Proc. TREC-8, number 500-246 in NIST Special Publication. NIST.
Named Entity Recognition for Astronomy Literature Tara Murphy and Tara McIntosh and James R. Curran School of Information Technologies University of Sydney NSW 2006, Australia {tm,tara,james}@it.usyd.edu.au Abstract We present a system for named entity recognition (ner) in astronomy journal articles. We have developed this system on a ne corpus comprising approximately 200,000 words of text from astronomy articles. These have been manually annotated with ∼40 entity types of interest to astronomers. We report on the challenges involved in extracting the corpus, defining entity classes and annotating scientific text. We investigate which features of an existing state-of-the-art Maximum Entropy approach perform well on astronomy text. Our system achieves an F-score of 87.8%. 1 Introduction Named entity recognition (ner) involves assigning broad semantic categories to entity references in text. While many of these categories do in fact refer to named entities, e.g. person and location, others are not proper nouns, e.g. date and money. However, they are all syntactically and/or semantically distinct and play a key role in Information Extraction (ie). ner is also a key component of Question Answering (qa) systems (Hirschman and Gaizauskas, 2001). State-of-the-art qa systems often have custom-built ner components with finer-grained categories than existing corpora (Harabagiu et al., 2000). For ie and qa systems, generalising entity references to broad semantic categories allows shallow extraction techniques to identify entities of interest and the relationships between them. Another recent trend is to move beyond the traditional domain of newspaper text to other corpora. In particular, there is increasing interest in extracting information from scientific documents, such as journal articles, especially in biomedicine (Hirschman et al., 2002). A key step in this process is understanding the entities of interest to scientists and building models to identify them in text. Unfortunately, existing models of language perform very badly on scientific text even for the categories which map directly between science and newswire, e.g. person. Scientific entities often have more distinctive orthographic structure which is not exploited by existing models. In this work we identify entities within astronomical journal articles. The astronomy domain has several advantages: firstly, it is representative of the physical sciences; secondly, the majority of papers are freely available in a format that is relatively easy to manipulate (L ATEX); thirdly, there are many interesting entity types to consider annotating; finally, there are many databases of astronomical objects that we will eventually exploit as gazetteer information. After reviewing comparable named entity corpora, we discuss aspects of astronomy that make it challenging for nlp. We then describe the corpus collection and extraction process, define the named entity categories and present some examples of interesting cases of ambiguity that come up in astronomical text. Finally, we describe experiments with retraining an existing Maximum Entropy tagger for astronomical named entities. Interestingly, some feature types that work well for newswire significantly degrade accuracy here. We also use the tagger to detect errors and inconsistencies in the annotated corpus. We plan to develop a much larger freely available astronomy ne corpus based on our experience described here. Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), pages 57–64. 57
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