NDLI: Probabilistic semantic similarity measurements for noisy short texts using Wikipedia entities

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Probabilistic semantic similarity measurements for noisy short texts using Wikipedia entities

Content Provider	ACM Digital Library
Author	Nakayama, Kotaro Hara, Takahiro Nishio, Shojiro Shirakawa, Masumi
Abstract	This paper describes a novel probabilistic method of measuring semantic similarity for real-world noisy short texts like microblog posts. Our method adds related Wikipedia entities to a short text as its semantic representation and uses the vector of entities for computing semantic similarity. Adding related entities to texts is generally a compound problem that involves the extraction of key terms, finding related entities for each key term, and the aggregation of related entities. Explicit Semantic Analysis (ESA), a popular Wikipedia-based method, solves these problems by summing the weighted vectors of related entities. However, this heuristic weighting highly depends on the rule of majority decision and is not suited to short texts that contain few key terms but many noisy terms. The proposed probabilistic method synthesizes these procedures by extending naive Bayes and achieves robust estimates of related Wikipedia entities for short texts. Experimental results on short text clustering using Twitter data indicated that our method outperformed ESA for short texts containing noisy terms.
Starting Page	903
Ending Page	908
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505600
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Short text clustering Semantic representation Semantic similarity Naive bayes
Content Type	Text
Resource Type	Article

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