Summary
This chapter shows how to derive postsemantic context based on vector representations of words (described in Chap.8). The core problem is to discover relevant word associations in relation to seed words in the utterance. This may involve uncovering implicit associations or reweighting explicit associations more highly. The set of such associations forms a part of ‘conversational implicature’. The chapter describes techniques for computing associations in a dimensional space that have shown promise in the literature. The goal is to provide some initial insights as to their usefulness for mining conversational implicature by applying them to a small set of email utterances.
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McArthur, R., Bruza, P. (2003). Discovery of Tacit Knowledge and Topical Ebbs and Flows Within the Utterances of an Online Community. In: Ohsawa, Y., McBurney, P. (eds) Chance Discovery. Advanced Information Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06230-2_9
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DOI: https://doi.org/10.1007/978-3-662-06230-2_9
Publisher Name: Springer, Berlin, Heidelberg
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