Learning the Scope of Negation via Shallow Semantic Parsing

Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pages 671–679, Beijing, August 2010 Learning the Scope of Negation via Shallow Semantic Parsing Junhui Li Guodong Zhou∗ Hongling Wang Qiaoming Zhu School of Computer Science and Technology Soochow University at Suzhou {lijunhui, gdzhou, redleaf, qmzhu}@suda.edu.cn ∗ Corresponding author Abstract In this paper we present a simplified shallow semantic parsing approach to learning the scope of negation (SoN). This is done by formulating it as a shallow semantic parsing problem with the negation signal as the predicate and the negation scope as its ar- guments. Our parsing approach to SoN learning differs from the state-of-the-art chunking ones in two aspects. First, we ex- tend SoN learning from the chunking level to the parse tree level, where structured syn- tactic information is available. Second, we focus on determining whether a constituent, rather than a word, is negated or not, via a simplified shallow semantic parsing frame- work. Evaluation on the BioScope corpus shows that structured syntactic information is effective in capturing the domination rela- tionship between a negation signal and its dominated arguments. It also shows that our parsing approach much outperforms the state-of-the-art chunking ones. 1 Introduction Whereas negation in predicate logic is well-defined and syntactically simple, negation in natural language is much complex. Gener- ally, learning the scope of negation involves two subtasks: negation signal finding and nega- tion scope finding. The former decides whether the words in a sentence are negation signals (i.e., words indicating negation, e.g., no, not, fail, rather than), where the semantic informa- tion of the words, rather than the syntactic in- formation, plays a critical role. The latter de- termines the sequences of words in the sen- tence which are negated by the given negation signal. Compared with negation scope finding, negation signal finding is much simpler and has been well resolved in the literature, e.g. with the accuracy of 95.8%-98.7% on the three subcorpora of the Bioscope corpus (Morante and Daelemans, 2009). In this paper, we focus on negation scope finding instead. That is, we assume golden negation signal finding. Finding negative assertions is essential in information extraction (IE), where in general, the aim is to derive factual knowledge from free text. For example, Vincze et al. (2008) pointed out that the extracted information within the scopes of negation signals should either be discarded or presented separately from factual information. This is especially important in the biomedical domain, where various linguistic forms are used extensively to express impressions, hypothesized explanations of experimental results or negative findings. Szarvas et al. (2008) reported that 13.45% of the sentences in the abstracts subcorpus of the BioScope corpus and 12.70% of the sentences in the full papers subcorpus of the Bioscope corpus contain negative assertions. In addition to the IE tasks in the biomedical domain, SoN learning has attracted more and more attention in some natural language processing (NLP) tasks, such as sentiment classification (Turney, 2002). For example, in the sentence “The chair is not comfortable …

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