Learning the Scope of Negation via Shallow Semantic Parsing

s Papers Clinical PCLB PCRB PCS PCLB PCRB PCS PCLB PCRB PCS autoparse(t&t) 91.97 87.82 80.88 85.45 67.20 59.26 97.48 88.30 85.89 autoparse(test) 92.71 88.33 81.84 87.57 68.78 62.70 97.48 87.73 85.21 oracle 99.72 94.59 94.37 98.94 84.13 83.33 99.89 98.39 98.39 Table 5: Performance (%) of negation scope finding on the three subcorpora by using automatic parser trained with 6,691 sentences in GTB1.0. Papers Clinical PCLB PCRB PCS PCLB PCRB PCS autoparse(t&t) 85.98 67.99 60.32 97.48 92.66 90.48 autoparse(test) 87.83 70.11 64.02 97.36 92.20 89.79 oracle 98.94 83.86 83.07 99.77 97.94 97.82 Table 6: Performance (%) of negation scope finding on the two subcorpora by using automatic parser trained with all the sentences in GTB1.0. Method Abstracts Papers Clinical M et al. (2008) 57.33 n/a n/a M & D (2009) 73.36 50.26 87.27 Our baseline 73.42 53.70 88.42 Our final system 81.84 64.02 89.79 Table 7: Performance comparison over the PCS measure (%) of our system with other state-of-the-art ones. Table 7 compares our performance in PCS measure with related work. It shows that even our baseline system with four basic features as presented in Table 1 performs better than Morante et al. (2008) and Morante and Daelemans(2009). This indicates the appropriateness of our simplified shallow semantic parsing approach and the effectiveness of structured syntactic information on negation scope finding. It also shows that our final system significantly outperforms the state-of-the-art ones using a chunking approach, especially on the abstracts and full papers subcorpora. However, the improvement on the clinical reports subcorpus is less apparent, partly due to the fact that the sentences in this subcorpus are much simpler (with average length of 6.6 words per sentence) and thus a chunking approach can achieve high performance. Following are two typical sentences from the clinical reports subcorpus, where the negation scope covers the whole sentence (except the period punctuation). Such sentences account for 57% of negation sentences in the clinical reports subcorpus. 6 Conclusion In this paper we have presented a simplified shallow semantic parsing approach to negation scope finding by formulating it as a shallow semantic parsing problem, which has been extensively studied in the past few years. In particular, we regard the negation signal as the predicate while mapping the negation scope into several constituents which are deemed as arguments of the negation signal. Evaluation on the Bioscope corpus shows the appropriateness of our shallow semantic parsing approach and that structured syntactic information plays a critical role in capturing the domination relationship between a negation signal and its negation scope. It also shows that our parsing approach much outperforms the state-of-the-art chunking ones. To our best knowledge, this is the first research on exploring negation scope finding via shallow semantic parsing. Future research will focus on joint learning of negation signal and its negation scope findings. Although Morante and Daelemans (2009) reported the performance of 95.8%-98.7% on negation signal finding, it lowers the performance of negation scope finding by about 7.29%-16.52% in PCS measure.