Full Paper (6 pages)
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Prior work on bias detection has predominantly relied on pre-compiled word lists. However, the effectiveness of pre-compiled word lists is challenged when the detection of bias not only depends on the word itself but also depends on the context in which the word resides. In this work, we train neural language models to generate vector space representation to capture the semantic and contextual infor- mation of the words as features in bias detection. We also use word vector representations produced by the GloVe algorithm as semantic features. We feed the semantic and contextual features to train a linguistic model for bias detection. We evaluate the linguistic model's performance on a Wikipedia-derived bias detection dataset and on a focused set of ambiguous terms. Our results show a relative F1 score improvement of up to 26.5% versus an existing approach, and a relative F1 score improvement of up to 14.7% on ambiguous terms.
In Proceedings of the Natural Language Processing meets Journalism IJCAI-16 Workshop, pages 57-62, New York City, July 10, 2016.
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