Abstract
Due to the limited length and freely constructed sentence structures, short text is different from normal text, which makes traditional algorithm of text representation does not work well on it. This paper proposes a model called Conceptual and Semantic Enrichment with Topic Model (CSET) by combining Biterm Topic Model (BTM), a widely used probabilistic topic model which is designed for short text with Probase, a large-scale probabilistic knowledge base. CSET is able to capture semantic relations between words to enrich short text. Our model enables large amount of applications that rely on semantic understanding of short text, including short text classification and word similarity measurement in context.
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