Weakly Supervised Learning for Hedge Classification in Scientific Literature

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 992–999, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Weakly Supervised Learning for Hedge Classification in Scientific Literature Ben Medlock Computer Laboratory University of Cambridge Cambridge, CB3 OFD benmedlock@cantab.net Ted Briscoe Computer Laboratory University of Cambridge Cambridge, CB3 OFD ejb@cl.cam.ac.uk Abstract We investigate automatic classification of speculative language (‘hedging’), biomedical text using weakly supervisedin machine learning. Our contributions include a precise description of the task with anno- tation guidelines, analysis and discussion, a probabilistic weakly supervised learning model, and experimental evaluation of the methods presented. We show that hedge classification is feasible using weakly supervised ML, and point toward avenues for future research. 1 Introduction The automatic processing of scientific papers using NLP and machine learning (ML) techniques is an increasingly important aspect of technical informat- ics. In the quest for a deeper machine-driven ‘under- standing’ of the mass of scientific literature, a fre- quently occuring linguistic phenomenon that must be accounted for is the use of hedging to denote propositions of a speculative nature. Consider the following: 1. Our results prove that XfK89 inhibits Felin-9. 2. Our results suggest that XfK89 might inhibit Felin-9. The second example contains a hedge, signaled by the use of suggest and might, which renders the proposition inhibit(XfK89→Felin-9) speculative. Such analysis would be useful in various applica- tions; for instance, consider a system designed to identify and extract interactions between genetic en- tities in the biomedical domain. Case 1 above pro- vides clear textual evidence of such an interaction and justifies extraction of inhibit(XfK89→Felin-9), whereas case 2 provides only weak evidence for such an interaction. Hedging occurs across the entire spectrum of sci- entific literature, though it is particularly common in the experimental natural sciences. In this study we consider the problem of learning to automatically classify sentences containing instances of hedging, given only a very limited amount of annotator- labelled ‘seed’ data. This falls within the weakly su- pervised ML framework, for which a range of tech- niques have been previously explored. The contri- butions of our work are as follows: 1. We provide a clear description of the prob- lem of hedge classification and offer an im- proved and expanded set of annotation guide- lines, which as we demonstrate experimentally are sufficient to induce a high level of agree- ment between independent annotators. 2. We discuss the specificities of hedge classifica- tion as a weakly supervised ML task. 3. We derive a probabilistic weakly supervised learning model and use it to motivate our ap- proach. 4. We analyze our learning model experimentally and report promising results for the task on a new publicly-available dataset.1 2 Related Work 2.1 Hedge Classification While there is a certain amount of literature within the linguistics community on the use of hedging in 1available from www.cl.cam.ac.uk/∼bwm23/ 992 scientific text, eg. (Hyland, 1994), there is little of direct relevance to the task of classifying speculative language from an NLP/ML perspective. The most clearly …

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