Clustering to find exemplar terms for keyphrase extraction

Liu, Zhiyuan; Li, Peng; Zheng, Yabin; Sun, Maosong

doi:10.3115/1699510.1699544

Cited by 254 publications

(218 citation statements)

References 13 publications

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“…Instead of just using the input document, the statisticbased methods, e.g., Wan'07 [32], SC [17], TPR [16], exploit some external resources. Wan'07 [32] combines sentence extraction and keyphrase extraction together based on the point that important sentences and keyphrases reinforce each other.…”

Section: Related Workmentioning

confidence: 99%

“…And the relations in word-to-word graph are established according to the mutual information between words using a large external corpus. SC [17] adopts a clustering-based method which aims at achieving good coverage of topics. Terms are single words after stopwords removed.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the ranking score, the top-K phrases are returned as keyphrases. The statistic-based methods, e.g., Wan'07 [32], SC [17], and TPR [16], explore some external text corpus to assist keyphrase extraction. Generally, they obtain external information from the statistical information in large corpus.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, they obtain external information from the statistical information in large corpus. For example, SC [17] uses the Wikipedia 1 articles, based on which several statistical distances can be computed, such as cosine similarity, Euclidean distance, Pointwise Mutual Information (PMI), and Normalized Google Similarity Distance (NGD) [5]. Then these statistical distances are used as the similarity metrics in term clustering and candidate phrases contain cluster exemplar terms are selected as keyphrases.…”

Section: Introductionmentioning

confidence: 99%

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Keyphrase Extraction Using Knowledge Graphs

Wei

Zheng

et al. 2017

Data Sci. Eng.

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Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence-based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence-based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduce more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-the-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Keyphrase Extraction Using Knowledge Graphs

Wei

Zheng

et al. 2017

Data Sci. Eng.

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show abstract

“…For example, removing stop words using a stop word list [8], using part of speech tags [9], and using N-grams [10]. Yoshida and Nakagawa [11] proposed a keyword extraction method for Japanese language documents.…”

Section: A Candidate Keyword Extractionmentioning

confidence: 99%

Automatic Keyword Extraction for Wikification of East Asian Language Documents

Horita¹,

Kimura²,

Maeda³

2016

IJCTE

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Abstract-In recent years, research on Wikification, which aims to promote the effective reuse the Wikipedia resources and the understanding of document contents, is attracting much attention. Wikification is a method to automatically extract keywords from a document, and to link them to an appropriate Wikipedia article. Wikification consists of two processes. First, we extract keywords from a document. Second, we identify the appropriate Wikipedia article for each of them. In this paper, we focus on the extraction of keywords from a document for Wikification. Research on Wikification has been conducted for documents in variety of languages. We focus on East Asian language documents and experiment with Japanese documents. Besides, we are planning to do the Wikification not only for documents in the same language but also for other languages (e.g. keywords in Japanese documents are linked to appropriate English Wikipedia articles).Our proposed method consists of two steps. First, we extract nouns from a document using a morphological analysis tool, and extract candidate keywords by a method called Top Consecutive Nouns Cohesion (TCNC). The TCNC connects continuous nouns and treat them as one compound word. Second, we rank the extracted candidate keywords using one of two measures for keyword importance, Dice coefficient or Keyphraseness.In our experiments of extracting appropriate keywords for Wikification in Japanese documents, our proposed method, especially the combination of TCNC and Keyphraseness, achieved the best results.

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SemGraph: Extracting keyphrases following a novel semantic graph‐based approach

Martínez-Romo

Araujo

Fernandez

2015

Asso for Info Science & Tech

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Keyphrases represent the main topics a text is about. In this article, we introduce SemGraph, an unsupervised algorithm for extracting keyphrases from a collection of texts based on a semantic relationship graph. The main novelty of this algorithm is its ability to identify semantic relationships between words whose presence is statistically significant. Our method constructs a co-occurrence graph in which words appearing in the same document are linked, provided their presence in the collection is statistically significant with respect to a null model. Furthermore, the graph obtained is enriched with information from WordNet. We have used the most recent and standardized benchmark to evaluate the system ability to detect the keyphrases that are part of the text. The result is a method that achieves an improvement of 5.3% and 7.28% in F measure over the two labeled sets of keyphrases used in the evaluation of SemEval-2010.

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Clustering to find exemplar terms for keyphrase extraction

Cited by 254 publications

References 13 publications

Keyphrase Extraction Using Knowledge Graphs

Keyphrase Extraction Using Knowledge Graphs

Automatic Keyword Extraction for Wikification of East Asian Language Documents

SemGraph: Extracting keyphrases following a novel semantic graph‐based approach

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