Detecting the Scope of Negation and Speculation in Biomedical Texts by Using Recursive Neural Network

Detecting the scope of speculation and negation inbiomedical texts is an important research topic in biomedical community. Previous work shows that syntax information is crucial for the task, and these work usually models it locally by extracting a set of manually-crafted features such as dependency paths. In this paper, we explore empirically a recursive neural network model, representing whole dependency trees globally and learning automatically syntactic features, to better incorporate dependency-based syntax information. Experiments on BioScope corpus show that our model achieves competitive performance, outperforming previous systems.

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

[2]  Yang Huang,et al.  A novel hybrid approach to automated negation detection in clinical radiology reports. , 2007, Journal of the American Medical Informatics Association : JAMIA.

[3]  Han Ren,et al.  Context-augmented convolutional neural networks for twitter sarcasm detection , 2018, Neurocomputing.

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

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

[6]  Yue Zhang,et al.  Deceptive Opinion Spam Detection Using Neural Network , 2016, COLING.

[7]  Bonnie L. Webber,et al.  Neural Networks For Negation Scope Detection , 2016, ACL.

[8]  Ilya M. Goldin,et al.  Learning to Detect Negation with ‘Not’ in Medical Texts , 2003 .

[9]  Yue Zhang,et al.  Context-Sensitive Twitter Sentiment Classification Using Neural Network , 2016, AAAI.

[10]  Richard Socher,et al.  A Neural Network for Factoid Question Answering over Paragraphs , 2014, EMNLP.

[11]  Dong-Hong Ji,et al.  Neural networks for deceptive opinion spam detection: An empirical study , 2017, Inf. Sci..

[12]  Wei Luo,et al.  Speculation and Negation Scope Detection via Convolutional Neural Networks , 2016, EMNLP.

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

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

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

[16]  Dan Klein,et al.  Accurate Unlexicalized Parsing , 2003, ACL.

[17]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[18]  Noriko Tomuro,et al.  Automatic Extraction of Lexico-Syntactic Patterns for Detection of Negation and Speculation Scopes , 2011, ACL.

[19]  Donghong Ji,et al.  Long short-term memory RNN for biomedical named entity recognition , 2017, BMC Bioinformatics.

[20]  Dong-Hong Ji,et al.  A topic-enhanced word embedding for Twitter sentiment classification , 2016, Inf. Sci..

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

[22]  Andrew Y. Ng,et al.  Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.

[23]  Quoc V. Le,et al.  Grounded Compositional Semantics for Finding and Describing Images with Sentences , 2014, TACL.

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

[25]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[26]  János Csirik,et al.  The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes , 2008, BMC Bioinformatics.

[27]  Yue Zhang,et al.  Improving Twitter Sentiment Classification Using Topic-Enriched Multi-Prototype Word Embeddings , 2016, AAAI.

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