A method for extracting technical terms using the modified weirdness measure

Kochetkova, Nataliya

doi:10.3103/s0005105515030036

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…Approaches, tools, algorithms, and methods for automatic term extraction: A systematic literature mapping 2015 Gaizauskas et al (2015), Khumalo (2015), Saneifar et al (2015), Gupta (2015), Pan and Zhao (2015), Kochetkova (2015), Lopes and Vieira (2015), Periñán-Pascual (2015), Gonçalves et al (2015), Guo et al (2015), Lahbib et al (2015), Astrakhantsev et al (2015), Bakar et al (2015), Liu et al (2015) 12,39% 2016…”

Section: Resultsmentioning

confidence: 99%

“…Gemkow et al (2018) use NLTK to perform tokenization, Part-of-Speech Tagging (POST), chunking, and lemmatization. NLTK is also used in other proposals such as Gaizauskas et al (2015), Kochetkova (2015), Bakar et al (2015), Liu et al (2015), Yu et al (2017), Giannakopoulos et al (2017) and Mykowiecka et al (2018). Its versatility for the construction of terminology extractors is highlighted.…”

Section: What Are the Tools Used For The Development Of Automatic Ter...mentioning

confidence: 99%

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Approaches, tools, algorithms, and methods for automatic term extraction: A systematic literature mapping

Andrade

Otálvaro

Jaramillo

et al. 2023

Preprint

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Automatic term extraction is a branch of Natural Language Processing (NLP) used to automatically generate lexicographic materials, such as glossaries, vocabularies, and dictionaries. It allows the creation of standard bases for building unified theories and translations between languages. Scientific literature shows great interest in the construction of automatic term extractors and includes several approaches, tools, algorithms, and methods that can be used for their construction; however, the number of articles in specialized databases is vast, and literature reviews are not recent. This paper presents a systematic literature mapping of the existing material for developing automatic term extractors to provide an overview of approaches, tools, algorithms, and methods used to create them. For this purpose, scientific articles in the domain published between 2015 and 2022 are reviewed and categorized. The mapping results show that among the most used approaches are statistical, with 21.85%; linguistic, with 9.75%; and hybrid, with 68.29%. In addition, there are various computational tools for terminology extraction where authors use different methods for their construction and whose results are measured under the criteria of precision and recall. Finally, 113 documents were selected to answer the research questions and to demonstrate how automatic term extractors are constructed. This paper presents a global summary of primary studies as an essential tool to approach this type of computational system construction.

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Section: Resultsmentioning

confidence: 99%

Section: What Are the Tools Used For The Development Of Automatic Ter...mentioning

confidence: 99%

Approaches, tools, algorithms, and methods for automatic term extraction: A systematic literature mapping

Andrade

Otálvaro

Jaramillo

et al. 2023

Preprint

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“…Term difficulty prediction (also referred to as term familiarity or term technicality prediction) can be seen as a subtask of automatic term extraction. For automatic term extraction, a major strand of methodologies are contrastive techniques, where a term candidate's distribution in a domain-specific text corpus is compared to the distribution in a reference corpus, for example a general-language corpus (Ahmad et al, 1994;Rayson and Garside, 2000;Drouin, 2003;Kit and Liu, 2008;Bonin et al, 2010;Kochetkova, 2015;Lopes et al, 2016;Mykowiecka et al, 2018, i.a.). Many term difficulty prediction studies rely on some variant of contrastive approaches, mostly frequency-based; notable exceptions are Zeng-Treitler et al (2008), who apply a contextual network, and Bouamor et al (2016), who use a likelihood ratio test based on two language models.…”

Section: Related Workmentioning

confidence: 99%

Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains

Hätty¹,

Bettinger²,

Dorna³

et al. 2021

Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

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Predicting the difficulty of domain-specific vocabulary is an important task towards a better understanding of a domain, and to enhance the communication between lay people and experts. We investigate German closed noun compounds and focus on the interaction of compound-based lexical features (such as frequency and productivity) and terminologybased features (contrasting domain-specific and general language) across word representations and classifiers. Our prediction experiments complement insights from classification using (a) manually designed features to characterise termhood and compound formation and (b) compound and constituent word embeddings. We find that for a broad binary distinction into easy vs. difficult general-language compound frequency is sufficient, but for a more fine-grained four-class distinction it is crucial to include contrastive termhood features and compound and constituent features.

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“…For standard term extraction, contrastive techniques represent one of the main strands of methodologies, by comparing a term candidate's frequencies in a domain-specific and a general-language corpus (Ahmad et al, 1994;Rayson and Garside, 2000;Drouin, 2003;Kit and Liu, 2008;Bonin et al, 2010;Kochetkova, 2015;Lopes et al, 2016;Mykowiecka et al, 2018, i.a.). Recent approaches use word embeddings trained separately on contrastive corpora; e.g.…”

Section: Related Workmentioning

confidence: 99%

Predicting Degrees of Technicality in Automatic Terminology Extraction

Hätty

Schlechtweg

Dorna

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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While automatic term extraction is a wellresearched area, computational approaches to distinguish between degrees of technicality are still understudied. We semi-automatically create a German gold standard of technicality across four domains, and illustrate the impact of a web-crawled general-language corpus on predicting technicality. When defining a classification approach that combines general-language and domain-specific word embeddings, we go beyond previous work and align vector spaces to gain comparative embeddings. We suggest two novel models to exploit general-vs. domain-specific comparisons: a simple neural network model with pre-computed comparative-embedding information as input, and a multi-channel model computing the comparison internally. Both models outperform previous approaches, with the multi-channel model performing best.

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A method for extracting technical terms using the modified weirdness measure

Cited by 4 publications

References 3 publications

Approaches, tools, algorithms, and methods for automatic term extraction: A systematic literature mapping

Approaches, tools, algorithms, and methods for automatic term extraction: A systematic literature mapping

Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains

Predicting Degrees of Technicality in Automatic Terminology Extraction

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