• Natasha J
• Karpaga Priyaa K
• Vijayrani J

Most short text embedding models typically represent each short text only using the literal meanings of the words, which make these models indiscriminative for the ubiquitous polysemy. In order to enhance the semantic representation capability of the short texts, a novel short text conceptualization algorithm is proposed to assign the associated concepts for each short text using Probase and Concept Net and then introduce the conceptualization results into learning the conceptual short text embeddings. Hence, this semantic representation is more expressive than some widely used text representation models such as the latent topic model. Wherein, the short text conceptualization algorithm used here is based on a novel co-ranking framework, enabling the words and concepts to fully interplay to derive the solid conceptualization of short texts. Afterwards, short text embedding models are extended with this conceptualized short text words and concepts to make more efficient prediction. The experiments on the real world datasets demonstrate that the proposed short text conceptualization algorithm (and conceptualized short text embedding model) are more effective than the state of the art methods.

Short text, Conceptualization, Knowledge base, Embedding, CBOW, Skipgram

"CONCEPTUALIZED SHORT TEXT EMBEDDING USING KNOWLEDGE BASE ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.671-682, December-2018, Available :http://www.jetir.org/papers/JETIR1812600.pdf

"CONCEPTUALIZED SHORT TEXT EMBEDDING USING KNOWLEDGE BASE ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp671-682, December-2018, Available at : http://www.jetir.org/papers/JETIR1812600.pdf