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- W3155114470 abstract "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." @default.
- W3155114470 created "2021-04-26" @default.
- W3155114470 creator A5002157838 @default.
- W3155114470 date "2018-06-01" @default.
- W3155114470 modified "2023-09-28" @default.
- W3155114470 title "Understand Short Texts by Harvesting and Analyzing Semantic Knowledge by considering the crucial ambiguous base" @default.
- W3155114470 hasPublicationYear "2018" @default.
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