Automatic Mapping Clinical Notes to Medical - RMIT University

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A problem arises with this scheme when it is used in conjunction with error-correction: wherever it is possible to use a proper name, hypothesising a proper name gives a higher aggregate score than hypothesising an error, all other things being equal. The problem is serious, because grammars typically allow proper names in many different places, in particular as preposed and postposed sentence adverbials functioning as addressee terms (see Knott et al. (2004)). To remedy this problem, it is important to attach a cost to the operation of hypothesising a proper name, comparable to that of hypothesising an error. In our (as-yet unimplemented) combined unknown-word and error-correction scheme, if there is an unknown word which can be interpreted as a proper name, the lexicon is updated prior to parsing, and perturbations are created as usual. A special unknown word cost is associated with the original utterance and with each of these perturbations, except any perturbations which alter the unknown word (and thus do not rely on the hypothesised lexical item). The unknown word cost is another number between 0 and 1, and the aggregate score of an interpretation is multiplied by this number when deciding amongst alternative interpretations. The number is set to be lower than the average perturbation score. If any perturbations of the unknown word survive the parsing process, they stand a good chance of being preferred over the proper name hypothesis, or at least being presented as alternatives to it. We will experiment with this extension to the error-correction algorithm in future work. References E Bender, D Flickinger, S Oepen, A Walsh, and T Baldwin. 2004. Arboretum: Using a precision grammar for grammar checking in CALL. In Proceedings of the In- STIL/ICALL Symposium, pages 83–87. E Brill and R Moore. 2000. An improved error model for noisy channel spelling correction. In Proceedings of ACL. A Copestake and D Flickinger. 2000. An open-source grammar development environment and broad-coverage English grammar using hpsg. In Proceedings of LREC 2000, Athens, Greece. A Copestake. 2000. The (new) LKB system. CSLI, Stanford University. J Foster and C Vogel. 2004. Parsing ill-formed text using an error grammar. Artificial Intelligence Review, 21(3– 4):269–291. 128 F Fouvry. 2003. Lexicon acquisition with a large-coverage unification-based grammar. In 10th Conference of the European Chapter of the Association for Computational Linguistics, Research notes and demos, Budapest, Hungary. R Golding and Y Schabes. 1996. Combining trigram-based and feature-based methods for context-sensitive spelling correction. In Proceedings of the Thirty-Fourth Annual Meeting of the Association for Computational Linguistics. J Hobbs, M Stickel, D Appelt, and P Martin. 1993. Interpretation as abduction. Artificial Intelligence, 63. A Knott and P Vlugter. 2003. Syntactic disambiguation using presupposition resolution. In Proceedings of the 4th Australasian Language Technology Workshop (ALTW2003), Melbourne. A Knott, I Bayard, and P Vlugter. 2004. Multi-agent humanmachine dialogue: issues in dialogue management and referring expression semantics. In Proceedings of the 8th Pacific Rim Conference on Artificial Intelligence (PRICAI 2004), pages 872–881, Auckland. Springer Verlag: Lecture Notes in AI. K Kukich. 1992. Techniques for automatically correcting words in text. Computing Surveys, 24(4):377–439. P Lurcock, P Vlugter, and A Knott. 2004. A framework for utterance disambiguation in dialogue. In Proceedings of the 2004 Australasian Language Technology Workshop (ALTW), pages 101–108, Macquarie University. P Lurcock. 2005. Techniques for utterance disambiguation in a human-computer dialogue system. MSc thesis, Dept of Computer Science, University of Otago. L Mangu and E Brill. 1997. Automatic rule acquisition for spelling correction. In Proceedings of the 14th International Conference on Machine Learning. W Menzel and I Schröder. 1998. Constraint-based diagnosis for intelligent language tutoring systems. L Michaud, K Mccoy, and L Stark. 2001. Modeling the acquisition of english: an intelligent CALL approach. In Proceedings of The 8th International Conference on User Modeling, pages 13–17, Sonthofen, Germany. N Slabbers. 2005. A system for generating teaching initiatives in a computer-aided language learning dialogue. Technical Report OUCS-2005-02, Department of Computer Science, University of Otago, Dunedin, New Zealand. E van der Ham. 2005. Diagnosing and responding to student errors in a dialogue-based computer-aided languagelearning system. Technical Report OUCS-2005-06, Department of Computer Science, University of Otago, Dunedin, New Zealand. M van Schagen and A Knott. 2004. Tauira: A tool for acquiring unknown words in a dialogue context. In Proceedings of the 2004 Australasian Language Technology Workshop (ALTW), pages 131–138, Macquarie University. P Vlugter, A Knott, and V Weatherall. 2004. A humanmachine dialogue system for CALL. In Proceedings of InSTIL/ICALL 2004: NLP and speech technologies in Advanced Language Learning Systems, pages 215–218, Venice.
Using Dependency-Based Features to Take the “Para-farce” out of Paraphrase Stephen Wan †‡ Mark Dras † Robert Dale † † Center for Language Technology Div of Information Communication Sciences Macquarie University Sydney, NSW 2113 swan,madras,rdale@ics.mq.edu.au Abstract As research in text-to-text paraphrase generation progresses, it has the potential to improve the quality of generated text. However, the use of paraphrase generation methods creates a secondary problem. We must ensure that generated novel sentences are not inconsistent with the text from which it was generated. We propose a machine learning approach be used to filter out inconsistent novel sentences, or False Paraphrases. To train such a filter, we use the Microsoft Research Paraphrase corpus and investigate whether features based on syntactic dependencies can aid us in this task. Like Finch et al. (2005), we obtain a classification accuracy of 75.6%, the best known performance for this corpus. We also examine the strengths and weaknesses of dependency based features and conclude that they may be useful in more accurately classifying cases of False Paraphrase. 1 Introduction In recent years, interest has grown in paraphrase generation methods. The use of paraphrase generation tools has been envisaged for applications ranging from abstract-like summarisation (see for example, Barzilay and Lee (2003), Daumé and Marcu (2005), Wan et al. (2005)), questionanswering (for example, Marsi and Krahmer (2005)) and Machine Translation Evaluation (for example, Bannard and Callison-Burch (2005) and Yves Lepage (2005)). These approaches all employ a loose definition of paraphrase attributable to Dras (1999), who defines a ‘paraphrase pair’ Cécile Paris ‡ ‡ Information and Communication Technologies CSIRO Sydney, Australia Cecile.Paris@csiro.au operationally to be “a pair of units of text deemed to be interchangeable”. Notably, such a definition of paraphrase lends itself easily to corpora based methods. Furthermore, what the more modern approaches share is the fact that often they generate new paraphrases from raw text not semantic representations. The generation of paraphrases from raw text is a specific type of what is commonly referred to as text-to-text generation (Barzilay and Lee, 2003). As techniques for generating paraphrases improve and basic concerns such as grammaticality are less of an issue, we are faced with an additional concern. That is, we must validate whether or not the generated novel sentence is in fact a paraphrase. It may be detrimental in some applications, for example abstract-like summarisation, to allow a novel sentence that is inconsistent with the content of the input text to be presented to the end user. As an example of the type of inconsistencies that can arise from paraphrase generation, in Figure 1, we present two examples of generated sentences. In each example, a sentence pair is presented in which the second sentence was generated from an input news article statistically using a four-gram language model and a probabilistic word selection module. Although other paraphrase generation approaches differ in their underlying mechanisms 1 , most generate a novel sentence that cannot be found verbatim in the input text. The generated second sentence of the example is intended to be a paraphrase of the article headline. One might be convinced that the first exam- 1 The details of the generation algorithm used for this example are peripheral to the focus of this paper and we direct the interested reader to Wan et al. (2005) for more details. Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), pages 129–136. 129
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Program Chairs: Committees Lawrence
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Towards the Evaluation of Referring
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