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Neural induction of a lexicon for fast and interpretable stance classification.

Clos, Jérémie; Wiratunga, Nirmalie


Jérémie Clos


Jorge Gracia

Francis Bond

John P. McCrae

Paul Buitelaar

Christian Chiarcos

Sebastian Hellmann


Large-scale social media classification faces the following two challenges: algorithms can be hard to adapt to Web-scale data, and the predictions that they provide are difficult for humans to understand. Those two challenges are solved at the cost of some accuracy by lexicon-based classifiers, which offer a white-box approach to text mining by using a trivially interpretable additive model. However current techniques for lexicon-based classification limit themselves to using hand-crafted lexicons, which suffer from human bias and are difficult to extend, or automatically generated lexicons, which are induced using point-estimates of some predefined probabilistic measure on a corpus of interest. In this work we propose a new approach to learn robust lexicons, using the backpropagation algorithm to ensure generalization power without sacrificing model readability. We evaluate our approach on a stance detection task, on two different datasets, and find that our lexicon outperforms standard lexicon approaches.

Start Date Jun 19, 2017
Publication Date May 27, 2017
Print ISSN 0302-9743
Publisher Springer (part of Springer Nature)
Pages 181-193
Series Title Lecture notes in computer science
Series Number 10318
Series ISSN 0302-9743
ISBN 9783319598871
Institution Citation CLOS, J. and WIRATUNGA, N. 2017. Neural induction of a lexicon for fast and interpretable stance classification. In Gracia, J., Bond, F., McCrae, J.P., Buitelaar, P., Chiarcos, C. and Hellmann, S. (eds.) Language, data and knowledge: proceedings of the 1st International conference on language, data and knowledge (LDK 2017), 19-20 June 2017, Galway, Ireland. Lecture notes in computer science, 10318. Cham: Springer [online], pages 181-193. Available from:
Keywords Decision function; Aggregation function; Computational graph; Sentiment lexicon; Pointwise mutual information


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