An Improved Automatic Term Recognition Method for Spanish

Barrón-Cedeño, Alberto; Sierra, Gerardo; Patrick, David M.; Ananiadou, Sophia

doi:10.1007/978-3-642-00382-0_10

Cited by 32 publications

(17 citation statements)

References 4 publications

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“…Another measure is F-TFIDF-C [16] that combines an ATE measure (C-value) and an AKE measure (TF-IDF) to extract terms obtaining better results than C-value. Also, the C-value measure was also applied to many different languages besides English, such as Japanese, Serbian, Slovenian, Polish, Chinese [11], Spanish [2], Arabic, and French [15]. That is why we take C-value and F-TFIDF-C as baselines for comparison in our experiments.…”

Section: Text Mining Approachesmentioning

confidence: 99%

Integration of linguistic and web information to improve biomedical terminology extraction

Lossio-Ventura¹,

Jonquet²,

Roche

et al. 2014

Proceedings of the 18th International Database Engineering &Amp; Applications Symposium on - IDEAS '14

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Comprehensive terminology is essential for a community to describe, exchange, and retrieve data. In multiple domain, the explosion of text data produced has reached a level for which automatic terminology extraction and enrichment is mandatory. Automatic Term Extraction (or Recognition) methods use natural language processing to do so. Methods featuring linguistic and statistical aspects as often proposed in the literature, solve some problems related to term extraction as low frequency, complexity of the multi-word term extraction, human effort to validate candidate terms. In contrast, we present two new measures for extracting and ranking muli-word terms from domain-specific corpora, covering the all mentioned problems. In addition we demonstrate how the use of the Web to evaluate the significance of a multi-word term candidate, helps us to outperform precision results obtain on the biomedical GENIA corpus with previous reported measures such as C-value.

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Section: Text Mining Approachesmentioning

confidence: 99%

Integration of linguistic and web information to improve biomedical terminology extraction

Lossio-Ventura¹,

Jonquet²,

Roche

et al. 2014

Proceedings of the 18th International Database Engineering &Amp; Applications Symposium on - IDEAS '14

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show abstract

“…C-value has been applied to different languages including Spanish [2]. This measure assigns a termhood to a candidate string, based on: the total frequency of occurrence of the candidate string in the corpus, on the frequency of the candidate string as part of other longer candidate terms, on the number of these longer candidate terms, and on the length of the candidate string (in number of words).…”

Section: Multiword Termsmentioning

confidence: 99%

Extraction of Semantic Relations from Opinion Reviews in Spanish

Galicia-Haro

Gelbukh

2014

Lecture Notes in Computer Science

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Abstract. We report research on semantic relations extraction to build taxonomies. The state of the art approaches are based on text corpus or on domain texts acquisition to accurately characterize the domain of interest. We analyzed the application of unsupervised methods for ontology building using a collection of opinion reviews in Spanish and the Web. We present some results and discuss the obtained relations.

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“…The c-value measure was initially proposed to express the unithood property; thus, it works with complex expressions. Barrón-Cedeño and his co-workers [50] adapted this measure in order to make it possible to express the termhood and, thus, apply it to unigrams (see Equation 22). …”

Section: The Hybrid Approachmentioning

confidence: 99%

A survey of automatic term extraction for Brazilian Portuguese

Conrado

Felippo

Pardo³

et al. 2014

J Braz Comput Soc

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Background: Term extraction is highly relevant as it is the basis for several tasks, such as the building of dictionaries, taxonomies, and ontologies, as well as the translation and organization of text data. Methods and Results:In this paper, we present a survey of the state of the art in automatic term extraction (ATE) for the Brazilian Portuguese language. In this sense, the main contributions and projects related to such task have been classified according to the knowledge they use: statistical, linguistic, and hybrid (statistical and linguistic). We also present a study/review of the corpora used in the term extraction in Brazilian Portuguese, as well as a geographic mapping of Brazil regarding such contributions, projects, and corpora, considering their origins. Conclusions: In spite of the importance of the ATE, there are still several gaps to be filled, for instance, the lack of consensus regarding the formal definition of meaning of 'term'. Such gaps are larger for the Brazilian Portuguese when compared to other languages, such as English, Spanish, and French. Examples of gaps for Brazilian Portuguese include the lack of a baseline ATE system, as well as the use of more sophisticated linguistic information, such as the WordNet and Wikipedia knowledge bases. Nevertheless, there is an increase in the number of contributions related to ATE and an interesting tendency to use contrasting corpora and domain stoplists, even though most contributions only use frequency, noun phrases, and morphosyntactic patterns.

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An Improved Automatic Term Recognition Method for Spanish

Cited by 32 publications

References 4 publications

Integration of linguistic and web information to improve biomedical terminology extraction

Integration of linguistic and web information to improve biomedical terminology extraction

Extraction of Semantic Relations from Opinion Reviews in Spanish

A survey of automatic term extraction for Brazilian Portuguese

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