Automatic term detection

Castellví, María Teresa Cabré i; Estopà, Rosa; Palatresi, Jordi Vivaldi

doi:10.1075/nlp.2.04cab

Cited by 70 publications

(4 citation statements)

References 15 publications

(17 reference statements)

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“…Due to the lack of semantically tagged resources and as shown in Cabré et al (2001) and Pazienza et al (2005), many indirect methods have been proposed to obtain the terms included in texts. Some of them are based on linguistic knowledge, as in Heid et al (1996), others use statistical measures, such as ANA (Enguehard and Pantera 1995).…”

Section: Term Extractionmentioning

confidence: 99%

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MHeTRep: A multilingual semantically tagged health terms repository

Vivaldi

Rodríguez

2022

Nat. Lang. Eng.

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This paper presents MHeTRep, a multilingual medical terminology and the methodology followed for its compilation. The multilingual terminology is organised into one vocabulary for each language. All the terms in the collection are semantically tagged with a tagset corresponding to the top categories of Snomed-CT ontology. When possible, the individual terms are linked to their equivalent in the other languages. Even though many NLP resources and tools claim to be domain independent, their application to specific tasks can be restricted to specific domains, otherwise their performance degrades notably. As the accuracy of NLP resources drops heavily when applied in environments different from which they were built, a tuning to the new environment is needed. Usually, having a domain terminology facilitates and accelerates the adaptation of general domain NLP applications to a new domain. This is particularly important in medicine, a domain living moments of great expansion. The proposed method takes Snomed-CT as starting point. From this point and using 13 multilingual resources, covering the most relevant medical concepts such as drugs, anatomy, clinical findings and procedures, we built a large resource covering seven languages totalling more than two million semantically tagged terms. The resulting collection has been intensively evaluated in several ways for the involved languages and domain categories. Our hypothesis is that MHeTRep can be used advantageously over the original resources for a number of NLP use cases and likely extended to other languages.

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Section: Term Extractionmentioning

confidence: 99%

“…A common limitation of most extractors is the lack of semantic knowledge. Notable exceptions for the medical domain are MetaMap (Aronson and Lang 2010) and YATE (Cabré et al 2001). Most approaches focus on technical domains in which specific resources are available l and term extraction is easier.…”

Section: Term Extractionmentioning

confidence: 99%

MHeTRep: A multilingual semantically tagged health terms repository

Vivaldi

Rodríguez

2022

Nat. Lang. Eng.

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“…Das manuelle Kodieren durch Experten stellt dabei sowohl eine subjektive als auch sehr zeitaufwändige Methode zur Bestimmung themenspezifischer Fachbegriffe dar. Aus diesem Grund werden bereits seit längerer Zeit Ansätze diskutiert, um diese fachlichen Termini automatisiert aus großen Datenmengen zu extrahieren (Castellví et al, 2001). Hier existieren verschiedene methodische Realisierungen um dabei Probleme, wie beispielsweise das Auffinden von Termini, die aus mehreren Wörtern bestehen, zu lösen (Heylen & De Hertog, 2017).…”

Section: Nlp-methoden Zur Bestimmung Fachspezifischen Vokabularsunclassified

Empirische Studien zur horizontalen Vernetzung in naturwissenschaftlichen Schulbüchern und zur Förderung situationalen Interesses im fächerübergreifenden Unterricht

Lewing¹

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“…Castellví, Bagot e Palatresi (2001) define "termo" como: "unidade terminológica obtida a partir de domínio especializado". Palatresi (2002) também define "termo" como "unidade terminológica que é utilizada para designar conceitos em um âmbito tematicamente restrito".…”

Section: Extração De Termosunclassified

Classificação semanticamente enriquecida por expressões do domínio

Scheicher¹

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Agradeço acima de tudo à Deus, por me permitir estar aqui nesse momento e ser o suporte de todos os passos da minha vida.Agradeço aos meus pais, minha mãe Magda Scheicher e ao meu falecido pai Jorge Scheicher, que me deram toda a base para chegar até aqui.Agradeço à minha esposa Tânia Scheicher, companheira de todos os meus momentos, dores, angústias, cansaço, alegrias e comemorações. Meu braço direito de uma longa jornada.Agradeço à minha orientadora, Profa. Dra. Solange Oliveira Rezende, por todos os ensinamentos, pela sua amizade e generosidade, pela oportunidade de pesquisa e pela confiança depositada em meu trabalho.Agradeço também aos diversos colegas do curso de doutorado, que estiveram presentes em minha vida e, de alguma forma, fizeram parte do meu crescimento profissional. Dentre eles, o agradecimento especial é para Roberta Akemi Sinoara, Dildre Georgiana Vasques e Flávia Lemos Sampaio Xavier.Agradeço a todos os professores e pesquisadores por todo empenho e dedicação nos ensinamentos.Agradecimentos especiais são direcionados à Universidade de São Paulo, aos professores e funcionários do ICMC-USP, aos membros da banca do exame de qualificação e de defesa.Agradeço ao apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Código de Financiamento 001, que apoiou financeiramente a realização desta pesquisa.

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Automatic term detection

Cited by 70 publications

References 15 publications

MHeTRep: A multilingual semantically tagged health terms repository

MHeTRep: A multilingual semantically tagged health terms repository

Empirische Studien zur horizontalen Vernetzung in naturwissenschaftlichen Schulbüchern und zur Förderung situationalen Interesses im fächerübergreifenden Unterricht

Classificação semanticamente enriquecida por expressões do domínio

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