Abstract
In the current internet environment, the issue of information credibility is crucial given that anyone can put up information on the Web freely. Hence, it is urge and necessary to develop computational approaches to distinguish reliable information from the unreliable, inaccurate one. In the year 2010, CoNLL proposed a new shared task of hedge detection, aiming to push un-certainty detection for natural language processing applications. Among the pro-posed approaches, sequence labeling models exhibit promising performance. However, only shallow features (e.g. word, lemma, POS tags, etc.) were explored in the existing research. In this paper, we aim to exploit the advantage of topical features in sequence labeling based hedge detection. The experimental results illustrate the effectiveness of the high-level features.
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Su, Q., Lou, H., Liu, P. (2013). Hedge Detection with Latent Features. In: Liu, P., Su, Q. (eds) Chinese Lexical Semantics. CLSW 2013. Lecture Notes in Computer Science(), vol 8229. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45185-0_46
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DOI: https://doi.org/10.1007/978-3-642-45185-0_46
Publisher Name: Springer, Berlin, Heidelberg
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