• Rajshri B Dattu
• Mr.S.M.Shinde

Short messages don't generally watch the linguistic structure of a written language. So not applicable to traditional natural language processing tools, ranging from part-of-speech tagging to dependency parsing. Short texts usually do not contain sufficient statistical signals or signs to support for text mining such as topic modeling. Short texts are more ambiguous and noisy, and are generated in an enormous volume, which further increases the trouble to deal with them. Here focus on semantic information in order to better understand short texts. In this work, build a model framework for short text understanding which abuses semantic knowledge gave by a well-known knowledge base and automatically harvested from a web corpus. Our knowledge-intensive approaches disrupt traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that focus on semantics in all these tasks.

Short messages, segmentation, natural language processing.

"Understand Short Texts by Harvesting and Analyzing Semantic Knowledge by considering the crucial ambiguous base", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.95-98, June-2018, Available :http://www.jetir.org/papers/JETIR1806515.pdf

"Understand Short Texts by Harvesting and Analyzing Semantic Knowledge by considering the crucial ambiguous base", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp95-98, June-2018, Available at : http://www.jetir.org/papers/JETIR1806515.pdf