Abstract
Explicit discourse relations in text are signalled by discourse connectives like since, because, however, etc. Identifying discourse connectives is a part of the bigger task called discourse parsing in which discourse coherence relations are extracted from text. In this paper we report improvements to the state-of-the-art for identifying explicit discourse connectives in the Penn Discourse Treebank and the Biomedical Discourse Relation Bank. These improvements have been achieved with maximum entropy (logistic regression) classifiers by combining machine learning features from previous approaches with new surface level features that capture information about a connective’s surrounding phrases and new syntactic features that add more information from the path in the syntax tree connecting the root to the connective and from the clause following the connective by means of its syntactic head.
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Ibn Faiz, S., Mercer, R.E. (2013). Identifying Explicit Discourse Connectives in Text. In: Zaïane, O.R., Zilles, S. (eds) Advances in Artificial Intelligence. Canadian AI 2013. Lecture Notes in Computer Science(), vol 7884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38457-8_6
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DOI: https://doi.org/10.1007/978-3-642-38457-8_6
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