Neural Networks For Negation Scope Detection

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 495–504, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics Neural Networks For Negation Scope Detection Federico Fancellu and Adam Lopez and Bonnie Webber School of Informatics University of Edinburgh 11 Crichton Street, Edinburgh f.fancellu[at]sms.ed.ac.uk, {alopez,bonnie}[at]inf.ed.ac.uk Abstract Automatic negation scope detection is a task that has been tackled using differ- ent classifiers and heuristics. Most sys- tems are however 1) highly-engineered, 2) English-specific, and 3) only tested on the same genre they were trained on. We start by addressing 1) and 2) using a neural network architecture. Results obtained on data from the *SEM2012 shared task on negation scope detection show that even a simple feed-forward neural network us- ing word-embedding features alone, per- forms on par with earlier classifiers, with a bi-directional LSTM outperforming all of them. We then address 3) by means of a specially-designed synthetic test set; in doing so, we explore the problem of de- tecting the negation scope more in depth and show that performance suffers from genre effects and differs with the type of negation considered. 1 Introduction Amongst different extra-propositional aspects of meaning, negation is one that has received a lot of attention in the NLP community. Previous work have focused in particular on automatically detect- ing the scope of negation, that is, given a nega- tive instance, to identify which tokens are affected by negation (§2). As shown in (1), only the first clause is negated and therefore we mark he and the car, along with the predicate was driving as inside the scope, while leaving the other tokens outside. (1) He was not driving the car and she left to go home. In the BioMedical domain there is a long line of research around the topic (e.g. Velldal et al. (2012) and Prabhakaran and Boguraev (2015)), given the importance of recognizing negation for information extraction from medical records. In more general domains, efforts have been more limited and most of the work centered around the *SEM2012 shared task on automatically detecting negation (§3), despite the recent interest (e.g. machine translation (Wetzel and Bond, 2012; Fancellu and Webber, 2014; Fancellu and Webber, 2015)). The systems submitted for this shared task, although reaching good overall performance are highly feature-engineered, with some relying on heuristics based on English (Read et al. (2012)) or on tools that are available for a limited number of languages (e.g. Basile et al. (2012), Packard et al. (2014)), which do not make them easily portable across languages. Moreover, the performance of these systems was only assessed on data of the same genre (stories from Conan Doyle’s Sherlock Holmes) but there was no attempt to test the approach on data of different genre. Given these shortcomings, we investigate whether neural network based sequence-to- sequence models (§ 4) are a valid alternative. The first advantage of neural networks-based methods for NLP is that we could perform classification by means of unsupervised word-embeddings features only, under the assumption …

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