Difference between revisions of "Textrank Algorithm by Exploiting Wikipedia for Short Text Keywords Extraction"
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== Overview == | == Overview == | ||
The characteristic of poor information of short text often makes the effect of traditional keywords extraction not as good as expected. In this paper, authors propose a graph-based ranking algorithm by exploiting [[Wikipedia]] as an external knowledge base for short text keywords extraction. To overcome the shortcoming of poor information of short text, authors introduce the Wikipedia to enrich the short text. Authors regard each entry of Wikipedia as a concept, therefore the [[semantic information]] of each word can be represented by the distribution of Wikipedia's concept. And authors measure the similarity between words by constructing the concept vector. Finally authors construct keywords matrix and use TextRank for keywords extraction. The comparative experiments with traditional TextRank and baseline algorithm show that method gets better precision, recall and F-measure value. It is shown that TextRank by exploiting Wikipedia is more suitable for short text keywords extraction. | The characteristic of poor information of short text often makes the effect of traditional keywords extraction not as good as expected. In this paper, authors propose a graph-based ranking algorithm by exploiting [[Wikipedia]] as an external knowledge base for short text keywords extraction. To overcome the shortcoming of poor information of short text, authors introduce the Wikipedia to enrich the short text. Authors regard each entry of Wikipedia as a concept, therefore the [[semantic information]] of each word can be represented by the distribution of Wikipedia's concept. And authors measure the similarity between words by constructing the concept vector. Finally authors construct keywords matrix and use TextRank for keywords extraction. The comparative experiments with traditional TextRank and baseline algorithm show that method gets better precision, recall and F-measure value. It is shown that TextRank by exploiting Wikipedia is more suitable for short text keywords extraction. | ||
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| + | == Embed == | ||
| + | === Wikipedia Quality === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Li, Wengen; Zhao, Jiabao. (2016). "[[Textrank Algorithm by Exploiting Wikipedia for Short Text Keywords Extraction]]".DOI: 10.1109/ICISCE.2016.151. | ||
| + | </nowiki> | ||
| + | </code> | ||
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| + | === English Wikipedia === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | {{cite journal |last1=Li |first1=Wengen |last2=Zhao |first2=Jiabao |title=Textrank Algorithm by Exploiting Wikipedia for Short Text Keywords Extraction |date=2016 |doi=10.1109/ICISCE.2016.151 |url=https://wikipediaquality.com/wiki/Textrank_Algorithm_by_Exploiting_Wikipedia_for_Short_Text_Keywords_Extraction}} | ||
| + | </nowiki> | ||
| + | </code> | ||
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| + | === HTML === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Li, Wengen; Zhao, Jiabao. (2016). &quot;<a href="https://wikipediaquality.com/wiki/Textrank_Algorithm_by_Exploiting_Wikipedia_for_Short_Text_Keywords_Extraction">Textrank Algorithm by Exploiting Wikipedia for Short Text Keywords Extraction</a>&quot;.DOI: 10.1109/ICISCE.2016.151. | ||
| + | </nowiki> | ||
| + | </code> | ||
Revision as of 14:35, 23 November 2019
Authors | Wengen Li Jiabao Zhao |
|---|---|
Publication date | 2016 |
DOI | 10.1109/ICISCE.2016.151 |
Links | Original |
Textrank Algorithm by Exploiting Wikipedia for Short Text Keywords Extraction - scientific work related to Wikipedia quality published in 2016, written by Wengen Li and Jiabao Zhao.
Overview
The characteristic of poor information of short text often makes the effect of traditional keywords extraction not as good as expected. In this paper, authors propose a graph-based ranking algorithm by exploiting Wikipedia as an external knowledge base for short text keywords extraction. To overcome the shortcoming of poor information of short text, authors introduce the Wikipedia to enrich the short text. Authors regard each entry of Wikipedia as a concept, therefore the semantic information of each word can be represented by the distribution of Wikipedia's concept. And authors measure the similarity between words by constructing the concept vector. Finally authors construct keywords matrix and use TextRank for keywords extraction. The comparative experiments with traditional TextRank and baseline algorithm show that method gets better precision, recall and F-measure value. It is shown that TextRank by exploiting Wikipedia is more suitable for short text keywords extraction.
Embed
Wikipedia Quality
Li, Wengen; Zhao, Jiabao. (2016). "[[Textrank Algorithm by Exploiting Wikipedia for Short Text Keywords Extraction]]".DOI: 10.1109/ICISCE.2016.151.
English Wikipedia
{{cite journal |last1=Li |first1=Wengen |last2=Zhao |first2=Jiabao |title=Textrank Algorithm by Exploiting Wikipedia for Short Text Keywords Extraction |date=2016 |doi=10.1109/ICISCE.2016.151 |url=https://wikipediaquality.com/wiki/Textrank_Algorithm_by_Exploiting_Wikipedia_for_Short_Text_Keywords_Extraction}}
HTML
Li, Wengen; Zhao, Jiabao. (2016). "<a href="https://wikipediaquality.com/wiki/Textrank_Algorithm_by_Exploiting_Wikipedia_for_Short_Text_Keywords_Extraction">Textrank Algorithm by Exploiting Wikipedia for Short Text Keywords Extraction</a>".DOI: 10.1109/ICISCE.2016.151.
