Evaluation of terms and term extraction systems

Vivaldi, Jorge; Rodríguez, Horacio

doi:10.1075/term.13.2.06viv

Cited by 54 publications

(31 citation statements)

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“…In the extraction of medicine terms and correlated areas, for instance, it is common to identify morphemes, whether radical or affix morphemes, with a Greek or Latin origin, such as stated by Vivaldi and Rodriguez [15] in 'artri/o-' ('arthr(o)-'), from the Greek 'arthros', in 'artrite' ('arthritis'). In ATE, it is possible to identify candidate terms from the domains of the nanoscience and nanotechnology based on the identification of morphemes such as nano-, as this term composes several simple terms (for example:…”

Section: The Linguistic Approachmentioning

confidence: 99%

“…Concerning ATE based on texts in Brazilian Portuguese, the best results also reach an 80% precision [9], when using the hybrid extractor EχATOLP [19]. The hybrid extractor YATE [15], proposed for the extraction of candidates from the Spanish corpora on the medicine domain, is one of the few in the literature that reaches a 98% precision rate. This extractor is characterized by the use of varied linguistic knowledge, such as morphological, syntactic, and semantic, together with statistical measures, considerably improving the extraction results.…”

Section: Introductionmentioning

confidence: 99%

“…There are many ATE investigations available in the literature [5][6][7][8][9][10][11][12][13][14][15][16][17]. However, they perform ATE using different scenarios (e.g., variation of the test corpora 1 and measures and evaluation conditions), which make it difficult to choose the best ATE system.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: The Linguistic Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A survey of automatic term extraction for Brazilian Portuguese

Conrado

Felippo

Pardo³

et al. 2014

J Braz Comput Soc

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“…There are also studies that consider different fixed values of candidates [2,[12][13][14]. Other studies explore a variation of result combinations (precision and recall, usually) [5,15]. There are studies that compare some measures used to extract terms [14,16,17].…”

Section: Related Workmentioning

confidence: 99%

The Main Challenge of Semi-Automatic Term Extraction Methods

Conrado¹,

Pardo²,

Rezende³

2015

Natural Language Processing and Cognitive Science

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Term extraction is the basis for many tasks such as building of taxonomies, ontologies and dictionaries, for translation, organization and retrieval of textual data. This paper studies the main challenge of semi-automatic term extraction methods, which is the difficulty to analyze the rank of candidates created by these methods. With the experimental evaluation performed in this work, it is possible to fairly compare a wide set of semi-automatic term extraction methods, which allows other future investigations. Additionally, we discovered which level of knowledge and threshold should be adopted for these methods in order to obtain good precision or F-measure. The results show there is not a unique method that is the best one for the three used corpora.

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“…These methods exploit data-centric, data-sensitive techniques for mining and organizing terms. Evaluation of these methods-as described in Vivaldi and Rodrïguez (2007) and Nazarenko and Zargayouna (2009)-is inherently a difficult task. Regardless of the employed metric and method for the performance comparison of CT algorithms, however, choosing a shared dataset consisting of a fixed set of documents-which can be accessed freely and easily-is a major step towards alleviating a number of obstacles in the evaluation process.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 4th International Workshop on Computational Terminology (Computerm)

2014

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IntroductionComputational Terminology covers an increasingly important aspect in Natural Language Processing areas such as text mining, information retrieval, information extraction, summarisation, textual entailment, document management systems, question-answering systems, ontology building, etc. Terminological information is paramount for knowledge mining from texts for scientific discovery and competitive intelligence. Scientific needs in fast growing domains (such as biomedicine, chemistry and ecology) and the overwhelming amount of textual data published daily demand that terminology is acquired and managed systematically and automatically; while in well established domains (such as law, economy, banking and music) the demand is on fine-grained analyses of documents for knowledge description and acquisition. Moreover, capturing new concepts leads to the acquisition and management of new knowledge.The aim of this fourth CompuTerm workshop is to bring together Natural Language Processing researchers to discuss recent advances in computational terminology and its impact in many NLP applications. The topics addressed in this workshop are wide ranging:• term extraction, recognition and filtering, which is the core of the terminological activity that lays basis for other terminological topics and tasks;• event recognition and extraction, that extends the notion of the terminological entity from terms meaning static units up to terms meaning procedural and dynamic processes;• acquisition of semantic relations among terms, which is also an important research topic as the acquisition of semantic relationships between terms finds applications such as the population and update of existing knowledge bases, definition of domain specific templates in information extraction and disambiguation of terms;• term variation management, that helps to deal with the dynamic nature of terms, their acquisition from heterogeneous sources, their integration, standardisation and representation for a large range of applications and resources, is also increasingly important, as one has to address this research problem when working with various controlled vocabularies, thesauri, ontologies and textual data. Term variation is also related to their paraphrases and reformulations, due to historical, regional, local or personal issues. Besides, the discovery of synonym terms or term clusters is equally beneficial to many NLP applications;• definition acquisition, that covers important research and aims to provide precise and nonambiguous description of terminological entities. Such definitions may contain elements necessary for the formal description of terms and concepts within ontologies;• consideration of the user expertise, that is becoming a new issue in the terminological activity, takes into account the fact that specialized domains contain notions and terms often nonunderstandable to non-experts or to laymen (such as patients within the medical area, or bank clients within banking and economy areas). This aspect, although related to s...

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Evaluation of terms and term extraction systems

Cited by 54 publications

References 9 publications

A survey of automatic term extraction for Brazilian Portuguese

A survey of automatic term extraction for Brazilian Portuguese

The Main Challenge of Semi-Automatic Term Extraction Methods

Proceedings of the 4th International Workshop on Computational Terminology (Computerm)

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