NDLI: An Incremental Weibo-Oriented Method for Unknown Word and Topic Extraction

Content Provider	IEEE Xplore Digital Library
Author	Qianren Liu Lei Wang
Copyright Year	2013
Description	Author affiliation: Sch. of Inf. & Commun. Eng., Beijing Univ. of Posts & Telecommun., Beijing, China (Qianren Liu; Lei Wang)
Abstract	Due to the great flexibility in wording and highly correlation between unknown words and unpredictable topics, which are exhibited in Chinese twitter (i.e. weibo) messages, it proposed a weibo-oriented method to detect unknown words and topics simultaneously. The method is efficient and precise, however, because of the adopted classical K-means algorithm, it cannot deal with weibo corpus with increasing size. In this paper, a modified version is presented to eliminate the limitation by incorporating incremental clustering mechanism. Based on the weibo characteristics, a new similarity measure is introduced, which takes into considerations the relevance of the recently identified unknown words. With this measure, the categories evolution is reasonably carried out in the process of incremental clustering to ensure the balance between the consistency and timeliness of the method. Experiments show that unknown words and topics can be effectively detected and tracked.
Starting Page	206
Ending Page	209
File Size	232737
Page Count	4
File Format	PDF
ISBN	9781479930128
DOI	10.1109/SKG.2013.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cosine similarity Dictionaries Correlation Unknown words extraction Semantics Clustering algorithms Incremental k-means Educational institutions Telecommunications Data mining Categories merge
Content Type	Text
Resource Type	Article

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