Difference between revisions of "Short Text Classification based on Wikipedia and Word2Vec"
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== Overview == | == Overview == | ||
Different from long texts, the [[features]] of Chinese short texts is much sparse, which is the primary cause of the low accuracy in the classification of short texts by using traditional classification methods. In this paper, a novel method was proposed to tackle the problem by expanding the features of short text based on [[Wikipedia]] and Word2vec. Firstly, build the semantic relevant concept sets of Wikipedia. Authors get the articles that have high relevancy with Wikipedia concepts and use the word2vec tools to measure the semantic [[relatedness]] between target concepts and related concepts. And then authors use the relevant concept sets to extend the short texts. Compared to traditional similarity measurement between concepts using statistical method, this method can get more accurate semantic relatedness. The experimental results show that by expanding the features of short texts, the classification accuracy can be improved. Specifically, method appeared to be more effective. | Different from long texts, the [[features]] of Chinese short texts is much sparse, which is the primary cause of the low accuracy in the classification of short texts by using traditional classification methods. In this paper, a novel method was proposed to tackle the problem by expanding the features of short text based on [[Wikipedia]] and Word2vec. Firstly, build the semantic relevant concept sets of Wikipedia. Authors get the articles that have high relevancy with Wikipedia concepts and use the word2vec tools to measure the semantic [[relatedness]] between target concepts and related concepts. And then authors use the relevant concept sets to extend the short texts. Compared to traditional similarity measurement between concepts using statistical method, this method can get more accurate semantic relatedness. The experimental results show that by expanding the features of short texts, the classification accuracy can be improved. Specifically, method appeared to be more effective. | ||
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| + | == Embed == | ||
| + | === Wikipedia Quality === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Wensen, Liu; Zewen, Cao; Jun, Wang; Xiao-yi, Wang. (2016). "[[Short Text Classification based on Wikipedia and Word2Vec]]".DOI: 10.1109/CompComm.2016.7924894. | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | === English Wikipedia === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | {{cite journal |last1=Wensen |first1=Liu |last2=Zewen |first2=Cao |last3=Jun |first3=Wang |last4=Xiao-yi |first4=Wang |title=Short Text Classification based on Wikipedia and Word2Vec |date=2016 |doi=10.1109/CompComm.2016.7924894 |url=https://wikipediaquality.com/wiki/Short_Text_Classification_based_on_Wikipedia_and_Word2Vec}} | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | === HTML === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Wensen, Liu; Zewen, Cao; Jun, Wang; Xiao-yi, Wang. (2016). &quot;<a href="https://wikipediaquality.com/wiki/Short_Text_Classification_based_on_Wikipedia_and_Word2Vec">Short Text Classification based on Wikipedia and Word2Vec</a>&quot;.DOI: 10.1109/CompComm.2016.7924894. | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | |||
| + | |||
| + | [[Category:Scientific works]] | ||
| + | [[Category:Chinese Wikipedia]] | ||
Latest revision as of 09:13, 17 October 2020
Authors | Liu Wensen Cao Zewen Wang Jun Wang Xiao-yi |
|---|---|
Publication date | 2016 |
DOI | 10.1109/CompComm.2016.7924894 |
Links | Original |
Short Text Classification based on Wikipedia and Word2Vec - scientific work related to Wikipedia quality published in 2016, written by Liu Wensen, Cao Zewen, Wang Jun and Wang Xiao-yi.
Overview
Different from long texts, the features of Chinese short texts is much sparse, which is the primary cause of the low accuracy in the classification of short texts by using traditional classification methods. In this paper, a novel method was proposed to tackle the problem by expanding the features of short text based on Wikipedia and Word2vec. Firstly, build the semantic relevant concept sets of Wikipedia. Authors get the articles that have high relevancy with Wikipedia concepts and use the word2vec tools to measure the semantic relatedness between target concepts and related concepts. And then authors use the relevant concept sets to extend the short texts. Compared to traditional similarity measurement between concepts using statistical method, this method can get more accurate semantic relatedness. The experimental results show that by expanding the features of short texts, the classification accuracy can be improved. Specifically, method appeared to be more effective.
Embed
Wikipedia Quality
Wensen, Liu; Zewen, Cao; Jun, Wang; Xiao-yi, Wang. (2016). "[[Short Text Classification based on Wikipedia and Word2Vec]]".DOI: 10.1109/CompComm.2016.7924894.
English Wikipedia
{{cite journal |last1=Wensen |first1=Liu |last2=Zewen |first2=Cao |last3=Jun |first3=Wang |last4=Xiao-yi |first4=Wang |title=Short Text Classification based on Wikipedia and Word2Vec |date=2016 |doi=10.1109/CompComm.2016.7924894 |url=https://wikipediaquality.com/wiki/Short_Text_Classification_based_on_Wikipedia_and_Word2Vec}}
HTML
Wensen, Liu; Zewen, Cao; Jun, Wang; Xiao-yi, Wang. (2016). "<a href="https://wikipediaquality.com/wiki/Short_Text_Classification_based_on_Wikipedia_and_Word2Vec">Short Text Classification based on Wikipedia and Word2Vec</a>".DOI: 10.1109/CompComm.2016.7924894.
