Thesaurus Based Term Ranking for Keyword Extraction

Gazendam, Luit; Wartena, Christian; Brussee, Rogier

doi:10.1109/dexa.2010.31

Cited by 21 publications

(6 citation statements)

References 16 publications

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“…Some methods utilise external knowledge to provide useful contextual information for extracting more semantically sensible keywords. For instance, Li and Wang (2014) proposed a TextRank-based AKE method that benefits from domain knowledge by using author-assigned keywords of scientific publications, and Gazendam et al (2010) proposed to use semantic relations between thesaurus terms for ranking candidate keywords without a reference corpus (Gazendam et al, 2010). Thesaurus relations have also been combined with machine learning techniques to improve performance of AKE methods (Hulth et al, 2001;Medelyan and Witten, 2006).…”

Section: Pos-tagging and Semantics In Akementioning

confidence: 99%

Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness

Altuncu¹,

Nurse²,

Xu³

et al. 2022

Preprint

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Automatic keyword extraction (AKE) has gained more importance with the increasing amount of digital textual data that modern computing systems process. It has various applications in information retrieval (IR) and natural language processing (NLP), including text summarisation, topic analysis and document indexing. This paper proposes a simple but effective post-processing-based universal approach to improve the performance of any AKE methods, via an enhanced level of semantic-awareness supported by PoS-tagging. To demonstrate the performance of the proposed approach, we considered word types retrieved from a PoS-tagging step and two representative sources of semantic information -specialised terms defined in one or more context-dependent thesauri, and named entities in Wikipedia. The above three steps can be simply added to the end of any AKE methods as part of a post-processor, which simply re-evaluate all candidate keywords following some context-specific and semantic-aware criteria. For five stateof-the-art (SOTA) AKE methods, our experimental results with 17 selected datasets showed that the proposed approach improved their performances both consistently (up to 100% in terms of improved cases) and significantly (between 10.2% and 53.8%, with an average of 25.8%, in terms of F1-score and across all five methods), especially when all the three enhancement steps

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Section: Pos-tagging and Semantics In Akementioning

confidence: 99%

Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness

Altuncu¹,

Nurse²,

Xu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…External knowledge based approaches use external knowledge sources such as terminological databases: GRISP, MeSH, etc. [5, 18–21], linguistic ressources (WordNet, etc. [22, 23]), article databanks (Wikipedia, etc.…”

Section: Related Workmentioning

confidence: 99%

Accurate keyphrase extraction by discriminating overlapping phrases

Haddoud

2014

Journal of Information Science

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In this paper we define the document phrase maximality index (DPM-index), a new measure to discriminate overlapping keyphrase candidates in a text document. As an application we developed a supervised learning system that uses 18 statistical features, among them the DPM-index and five other new features. We experimentally compared our results with those of 21 keyphrase extraction methods on SemEval-2010/Task-5 scientific articles corpus. When all the systems extract 10 keyphrases per document, our method enhances by 13% the F-score of the best system. In particular, the DPM-index feature increases the F-score of our keyphrase extraction system by a rate of 9%. This makes the DPM-index contribution comparable to that of the well-known TFIDF measure on such a system.

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“…However, these methods do not consider various aspects of words, so the chosen keywords may cover the content of documents in limited aspects. There are also some other types of research that use external data such as a thesaurus or ontology [20–22]. A thesaurus and ontology can be a good source as a secondary knowledge for extracting keywords, but they may carry a high cost of construction.…”

Section: Related Workmentioning

confidence: 99%

Keyword extraction for blogs based on content richness

Park

Kim

Lee

2013

Journal of Information Science

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In this paper, a method is proposed to extract topic keywords of blogs, based on the richness of content. If a blog includes rich content related to a topic word, the word can be considered as a keyword of the blog. For this purpose, a new measure, richness, is proposed, which indicates how much a blog covers the trendy subtopics of a keyword. In order to obtain trendy subtopics of keywords, we use outside topical context data – the web. Since the web includes various and trendy information, we can find popular and trendy content related to a topic. For each candidate keyword, a set of web documents is retrieved by Google, and the subtopics found in the web documents are modelled by a probabilistic approach. Based on the subtopic models, the proposed method evaluates the richness of blogs for candidate keywords, in terms of how much a blog covers the trendy subtopics of keywords. If a blog includes various contents on a word, the word needs to be chosen as one of the keywords of the blog. In the experiments, the proposed method is compared with various methods, and shows better results, in terms of hit count, trendiness and consistency.

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Thesaurus Based Term Ranking for Keyword Extraction

Cited by 21 publications

References 16 publications

Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness

Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness

Accurate keyphrase extraction by discriminating overlapping phrases

Keyword extraction for blogs based on content richness

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