Chinese Sentence-level Event Factuality Identification with Recursive Neural Network

Sentence-level event factuality identification (SEFI) aims to identify the factuality of an event presented in a sentence. Recent neural network-based approaches have demonstrated the efficacy of the shortest dependency path, but these methods lack semantic information compared with continuous text fragments and may lead to the omission of useful information. In addition, dependency paths are relatively flat. So far, most previous work focused on English datasets, and neglected Chinese tasks. And the existing Chinese SEFI methods ignore the syntactic information. To overcome the above issues, we propose a Chinese event factuality identification model based on dependency trees. We adopt a recursive neural network-based module that fuses event selected predicates, degree words, negative words, and event triggers to capture long-range relations among them. Experimental results on the Chinese event factuality corpus show that our proposed method outperforms other baselines.

[1]  Donghong Ji,et al.  Negation and speculation scope detection using recursive neural conditional random fields , 2020, Neurocomputing.

[2]  Guodong Zhou,et al.  Chinese Event Factuality Detection , 2019, NLPCC.

[3]  Dejing Dou,et al.  Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures , 2019, ACL.

[4]  Yue Zhang,et al.  Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification , 2018, IJCAI.

[5]  Rachel Rudinger,et al.  Neural Models of Factuality , 2018, NAACL.

[6]  Sunghwan Mac Kim,et al.  Demographic Inference on Twitter using Recursive Neural Networks , 2017, ACL.

[7]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[8]  Simon Clematide,et al.  How Factuality Determines Sentiment Inferences , 2016, *SEMEVAL.

[9]  Xu Sun,et al.  Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation , 2016, ACL.

[10]  Peifeng Li,et al.  A two-step approach for event factuality identification , 2015, 2015 International Conference on Asian Language Processing (IALP).

[11]  Yejin Choi,et al.  Event Detection and Factuality Assessment with Non-Expert Supervision , 2015, EMNLP.

[12]  Yahui Chen,et al.  Convolutional Neural Network for Sentence Classification , 2015 .

[13]  Halil Kilicoglu,et al.  A Compositional Interpretation of Biomedical Event Factuality , 2015 .

[14]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[15]  Jordan L. Boyd-Graber,et al.  A Neural Network for Factoid Question Answering over Paragraphs , 2014, EMNLP.

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

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

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

[19]  Ido Dagan,et al.  TruthTeller: Annotating Predicate Truth , 2013, NAACL.

[20]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  James Pustejovsky,et al.  Are You Sure That This Happened? Assessing the Factuality Degree of Events in Text , 2012, CL.

[22]  Christopher Potts,et al.  Did It Happen? The Pragmatic Complexity of Veridicality Assessment , 2012, CL.

[23]  Dragomir R. Radev,et al.  Rumor has it: Identifying Misinformation in Microblogs , 2011, EMNLP.

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

[25]  Owen Rambow,et al.  Automatic Committed Belief Tagging , 2010, COLING.

[26]  Weiwei Guo,et al.  Committed Belief Annotation and Tagging , 2009, Linguistic Annotation Workshop.

[27]  Qiaoming Zhu,et al.  End-to-End Event Factuality Identification via Hybrid Neural Networks , 2020, CCKS.

[28]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[29]  Peifeng Li,et al.  Identifying Chinese Event Factuality with Convolutional Neural Networks , 2017, CLSW.

[30]  James Pustejovsky,et al.  A factuality profiler for eventualities in text , 2008 .

[31]  James Pustejovsky,et al.  Annotating and Recognizing Event Modality in Text , 2006, FLAIRS.

[32]  C. Condoravdi,et al.  Computing relative polarity for textual inference , 2006 .