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Contextual Embedding for Distributed Representations of Entities in a Text Corpus
Knowledge Discovery and Data Mining
Distributed representations of textual elements in low dimensional vector space to capture context has gained great attention recently. Current state-of-the-art word embedding techniques compute distributed representations using co-occurrences of words within a contextual window discounting the flexibility to incorporate other contextual phenomena like temporal, geographical, and topical contexts. In this paper, we present a flexible framework that has the ability to leverage temporal,dblp:conf/kdd/KaderBNH16 fatcat:tkq4xtop2bduvkrx6avsj3hov4