Deep learning for conflicting statements detection in text

9 Background. Automatic contradiction detection or conflicting statements detection in text consists of identifying discrepancy, inconsistency and defiance in text and has several real world applications in questions and answering systems, multi-document summarization, dispute detection and finder in news, and detection of contradictions in opinions and sentiments on social media. Automatic contradiction detection is a technically challenging natural language processing problem. Contradiction detection between sources of text or two sentence pairs can be framed as a classification problem. 10

[1]  Kalina Bontcheva,et al.  Monolingual Social Media Datasets for Detecting Contradiction and Entailment , 2016, LREC.

[2]  Sanda M. Harabagiu,et al.  Negation, Contrast and Contradiction in Text Processing , 2006, AAAI.

[3]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[4]  T. Veale.  Round Up The Usual Suspects: Knowledge-Based Metaphor Generation , 2016 .

[5]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[6]  Rabab Kreidieh Ward,et al.  Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[7]  Themis Palpanas,et al.  Scalable discovery of contradictions on the web , 2010, WWW '10.

[8]  Mikalai Tsytsarau.  Scalable Detection of Sentiment-Based Contradictions , 2011 .

[9]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[10]  Richard Tzong-Han Tsai,et al.  Validating Contradiction in Texts Using Online Co-Mention Pattern Checking , 2012, TALIP.

[11]  John G. Taylor,et al.  Vector Machines , 2002, Neural Networks and the Financial Markets.

[12]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[13]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[14]  Christopher D. Manning,et al.  Identifying Conflicting Information in Texts , 2009 .

[15]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[16]  John Mark Agosta,et al.  Highlighting disputed claims on the web , 2010, WWW '10.

[17]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[18]  Alexander J. Smola,et al.  Classification in a normalized feature space using support vector machines , 2003, IEEE Trans. Neural Networks.

[19]  Doug Downey,et al.  It’s a Contradiction – no, it’s not: A Case Study using Functional Relations , 2008, EMNLP.

[20]  Tracy Hall,et al.  Researcher Bias: The Use of Machine Learning in Software Defect Prediction , 2014, IEEE Transactions on Software Engineering.

[21]  Sven Apel,et al.  Views on Internal and External Validity in Empirical Software Engineering , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[22]  Kentaro Inui,et al.  Identifying Contradictory and Contrastive Relations between Statements to Outline Web Information on a Given Topic , 2010, COLING.

[23]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[24]  Christopher D. Manning,et al.  Finding Contradictions in Text , 2008, ACL.