Speculation and Negation Scope Detection via Convolutional Neural Networks

Speculation and negation are important information to identify text factuality. In this paper, we propose a Convolutional Neural Network (CNN)-based model with probabilistic weighted average pooling to address speculation and negation scope detection. In particular, our CNN-based model extracts those meaningful features from various syntactic paths between the cues and the candidate tokens in both constituency and dependency parse trees. Evaluation on BioScope shows that our CNN-based model significantly outperforms the state-ofthe-art systems on Abstracts, a sub-corpus in BioScope, and achieves comparable performances on Clinical Records, another subcorpus in BioScope.

[1]  Stephan Oepen,et al.  Speculation and Negation: Rules, Rankers, and the Role of Syntax , 2012, CL.

[2]  Qun Liu,et al.  Encoding Source Language with Convolutional Neural Network for Machine Translation , 2015, ACL.

[3]  Rob Fergus,et al.  Stochastic Pooling for Regularization of Deep Convolutional Neural Networks , 2013, ICLR.

[4]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[5]  Christopher D. Manning,et al.  Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks , 2015, ACL.

[6]  Guodong Zhou,et al.  Learning the Scope of Negation via Shallow Semantic Parsing , 2010, COLING.

[7]  Wendy W. Chapman,et al.  Evaluation of negation phrases in narrative clinical reports , 2001, AMIA.

[8]  Roser Morante,et al.  A Metalearning Approach to Processing the Scope of Negation , 2009, CoNLL.

[9]  Nigel Collier,et al.  The GENIA project: corpus-based knowledge acquisition and information extraction from genome research papers , 1999, EACL.

[10]  Carl Vogel,et al.  Proceedings of the 16th International Conference on Computational Linguistics , 1996, COLING 1996.

[11]  Yaojie Lu,et al.  Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition , 2015, EMNLP.

[12]  Jun'ichi Tsujii,et al.  Syntax Annotation for the GENIA Corpus , 2005, IJCNLP.

[13]  Guodong Zhou,et al.  Tree Kernel-based Negation and Speculation Scope Detection with Structured Syntactic Parse Features , 2013, EMNLP.

[14]  Andreas Vlachos,et al.  Detecting Speculative Language Using Syntactic Dependencies and Logistic Regression , 2010, CoNLL Shared Task.

[15]  Roser Morante,et al.  Learning the Scope of Hedge Cues in Biomedical Texts , 2009, BioNLP@HLT-NAACL.

[16]  Jun Zhao,et al.  Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks , 2015, ACL.

[17]  Erik Velldal,et al.  UiO 2: Sequence-labeling Negation Using Dependency Features , 2012, *SEMEVAL.

[18]  Stephan Oepen,et al.  Syntactic Scope Resolution in Uncertainty Analysis , 2010, COLING.

[19]  Dragomir R. Radev,et al.  Detecting Speculations and their Scopes in Scientific Text , 2009, EMNLP.

[20]  János Csirik,et al.  The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts , 2008, BioNLP.

[21]  Zhi Jin,et al.  Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths , 2015, EMNLP.

[22]  Roser Morante,et al.  Learning the Scope of Negation in Biomedical Texts , 2008, EMNLP.

[23]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[24]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[25]  Jun Zhao,et al.  Relation Classification via Convolutional Deep Neural Network , 2014, COLING.

[26]  Dongyan Zhao,et al.  Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling , 2015, EMNLP.

[27]  Ralph Grishman,et al.  Event Detection and Domain Adaptation with Convolutional Neural Networks , 2015, ACL.

[28]  Bowen Zhou,et al.  Dependency-based Convolutional Neural Networks for Sentence Embedding , 2015, ACL.

[29]  Xiaolong Wang,et al.  A Cascade Method for Detecting Hedges and their Scope in Natural Language Text , 2010, CoNLL Shared Task.

[30]  Zhengdong Lu,et al.  Context-Dependent Translation Selection Using Convolutional Neural Network , 2015, ACL.