ITEXT-BIO: Intelligent Term EXTraction for BIOmedical analysis

AbstractHere, we introduce ITEXT-BIO, an intelligent process for biomedical domain terminology extraction from textual documents and subsequent analysis. The proposed methodology consists of two complementary approaches, including free and driven term extraction. The first is based on term extraction with statistical measures, while the second considers morphosyntactic variation rules to extract term variants from the corpus. The combination of two term extraction and analysis strategies is the keystone of ITEXT-BIO. These include combined intra-corpus strategies that enable term extraction and analysis either from a single corpus (intra), or from corpora (inter). We assessed the two approaches, the corpus or corpora to be analysed and the type of statistical measures used. Our experimental findings revealed that the proposed methodology could be used: (1) to efficiently extract representative, discriminant and new terms from a given corpus or corpora, and (2) to provide quantitative and qualitative analyses on these terms regarding the study domain.

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Two approaches to compilation of bilingual multi-word terminology lists from lexical resources

Natural Language Engineering ◽

10.1017/s1351324919000615 ◽

2020 ◽

Vol 26 (4) ◽

pp. 455-479

Author(s):

Branislava Šandrih ◽

Cvetana Krstev ◽

Ranka Stanković

Keyword(s):

Information Science ◽

Target Language ◽

Support Vector ◽

Word Alignment ◽

Lexical Resources ◽

Terminology Extraction ◽

Source Language ◽

Term Extraction ◽

Two Parameters ◽

Shallow Parser

AbstractIn this paper, we present two approaches and the implemented system for bilingual terminology extraction that rely on an aligned bilingual domain corpus, a terminology extractor for a target language, and a tool for chunk alignment. The two approaches differ in the way terminology for the source language is obtained: the first relies on an existing domain terminology lexicon, while the second one uses a term extraction tool. For both approaches, four experiments were performed with two parameters being varied. In the experiments presented in this paper, the source language was English, and the target language Serbian, and a selected domain was Library and Information Science, for which an aligned corpus exists, as well as a bilingual terminological dictionary. For term extraction, we used the FlexiTerm tool for the source language and a shallow parser for the target language, while for word alignment we used GIZA++. The evaluation results show that for the first approach the F1 score varies from 29.43% to 51.15%, while for the second it varies from 61.03% to 71.03%. On the basis of the evaluation results, we developed a binary classifier that decides whether a candidate pair, composed of aligned source and target terms, is valid. We trained and evaluated different classifiers on a list of manually labeled candidate pairs obtained after the implementation of our extraction system. The best results in a fivefold cross-validation setting were achieved with the Radial Basis Function Support Vector Machine classifier, giving a F1 score of 82.09% and accuracy of 78.49%.

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A statistical approach to term extraction

International Journal of English Studies ◽

10.6018/ijes/2011/2/149691 ◽

2011 ◽

Vol 11 (2) ◽

pp. 159 ◽

Cited By ~ 5

Author(s):

Rogelio Nazar

Keyword(s):

Empirical Study ◽

Comparative Evaluation ◽

Statistical Approach ◽

Statistical Properties ◽

Automatic Identification ◽

Training Phase ◽

Terminology Extraction ◽

Term Extraction ◽

Medical Terms

This paper argues in favor of a statistical approach to terminology extraction, general to all languages but with language specific parameters. In contrast to many application-oriented terminology studies, which are focused on a particular language and domain, this paper adopts some general principles of the statistical properties of terms and a method to obtain the corresponding language specific parameters. This method is used for the automatic identification of terminology and is quantitatively evaluated in an empirical study of English medical terms. The proposal is theoretically and computationally simple and disregards resources such as linguistic or ontological knowledge. The algorithm learns to identify terms during a training phase where it is shown examples of both terminological and non-terminological units. With these examples, the algorithm creates a model of the terminology that accounts for the frequency of lexical, morphological and syntactic elements of the terms in relation to the non-terminological vocabulary. The model is then used for the later identification of new terminology in previously unseen text. The comparative evaluation shows that performance is significantly higher than other well-known systems.

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Improving term extraction with linguistic analysis in the biomedical domain

Research in Computing Science ◽

10.13053/rcs-70-1-12 ◽

2013 ◽

Vol 70 (1) ◽

pp. 157-172 ◽

Cited By ~ 4

Author(s):

Wiktoria Golik ◽

Robert Bossy ◽

Zorana Ratkovic ◽

Claire Nédellec

Keyword(s):

Linguistic Analysis ◽

Biomedical Domain ◽

Term Extraction

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Terminology extraction for global content management

Terminology ◽

10.1075/term.9.1.04ber ◽

2003 ◽

Vol 9 (1) ◽

pp. 51-69 ◽

Cited By ~ 3

Author(s):

Arendse Bernth ◽

Michael McCord ◽

Kara Warburton

Keyword(s):

Content Management ◽

Information Industry ◽

Timely Manner ◽

Translation Process ◽

Terminology Extraction ◽

Translation Quality ◽

Term Extraction ◽

Translation Services ◽

Key Terms

The role of terminology in content management has often been underrated. Term extraction has been identified by the information industry as an area requiring focus. Term extraction benefits both the content authoring and the translation process. Supplying key product terms to translation services several weeks before the actual translation begins reduces translation time, improves translation quality, and saves effort (and thus money) by reducing duplication of work. Getting the key terms ready in a timely manner can be difficult without some automation. This paper describes the process of proposing, designing, developing, and deploying a terminology extraction tool. The tool extracts nouns and noun groups, excludes non-translatable terms and known product terms, and displays a context for each extracted item. This is done based on full parsing of the text with a broad-coverage parser. The tool is made available to users on a Web server.

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Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase

Studies About Languages ◽

10.5755/j01.sal.1.39.29156 ◽

2021 ◽

pp. 85-92

Author(s):

Sigita Rackevičienė ◽

Liudmila Mockienė ◽

Andrius Utka ◽

Aivaras Rokas

Keyword(s):

Methodological Approach ◽

Open Data ◽

Methodological Framework ◽

Terminology Extraction ◽

Comparable Corpora ◽

Term Extraction ◽

Parallel Data ◽

Data Ecosystem ◽

Manual Input ◽

Theoretical Considerations

The aim of the paper is to present a methodological framework for the development of an English-Lithuanian bilingual termbase in the cybersecurity domain, which can be applied as a model for other language pairs and other specialised domains. It is argued that the presented methodological approach can ensure creation of high-quality bilingual termbases even with limited available resources. The paper touches upon the methods and problems of dataset (corpora) compilation, terminology annotation, automatic bilingual term extraction (BiTE) and alignment, knowledge-rich context extraction, and linguistic linked open data (LLOD) technologies. The paper presents theoretical considerations as well as the arguments on the effectiveness of the described methods. The theoretical analysis and a pilot study allow arguing that: 1) a combination of parallel and comparable corpora enable to considerably expand the amount and variety of data sources that can be used for terminology extraction; this methodology is especially important for less-resourced languages which often lack parallel data; 2) deep learning systems trained by using manually annotated data (gold standard corpora) allow effective automatization of extraction of terminological data and metadata, which enables to regularly update termbases with minimised manual input; 3) LLOD technologies enable to integrate the terminological data into the global linguistic data ecosystem and make it reusable, searchable and discoverable across the Web.

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Development of Curriculum Design Support System Based on Word Embedding and Terminology Extraction

Electronics ◽

10.3390/electronics9040608 ◽

2020 ◽

Vol 9 (4) ◽

pp. 608 ◽

Cited By ~ 1

Author(s):

HoSung Woo ◽

JaMee Kim ◽

WonGyu Lee

Keyword(s):

Programming Languages ◽

Curriculum Design ◽

The Body ◽

High Rate ◽

Terminology Extraction ◽

Entry Level ◽

Term Extraction ◽

Design Support System ◽

Standardized Terminology ◽

Technical Terms

The principles of computer skills have been included in primary and secondary educated since the early 2000s, and the reform of curricula is related to the development of IT. Therefore, curricula should reflect the latest technological trends and needs of society. The development of a curriculum involves the subjective judgment of a few experts or professors to extract knowledge from several similar documents. More objective extraction needs to be based on standardized terminology, and professional terminology can help build content frames for organizing curricula. The purpose of this study is to develop a smart system for extracting terms from the body of computer science (CS) knowledge and organizing knowledge areas. The extracted terms are composed of semantically similar knowledge areas, using the word2vec model. We analyzed a higher-education CS standards document and compiled a dictionary of technical terms with a hierarchical clustering structure. Based on the developed terminology dictionary, a specialized system is proposed to enhance the efficiency and objectivity of terminology extraction. The analysis of high school education courses in India and Israel using the technical term extraction system found that (1) technical terms for Software Development Fundamentals were extracted at a high rate in entry-level courses, (2) in advanced courses, the ratio of technical terms in the areas of Architecture and Organization, Programming Languages, and Software Engineering areas was high, and (3) electives that deal with advanced content had a high percentage of technical terms related to information systems.

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Extracting bilingual terms from the Web

Terminology ◽

10.1075/term.21.2.04gai ◽

2015 ◽

Vol 21 (2) ◽

pp. 205-236 ◽

Cited By ~ 1

Author(s):

Robert Gaizauskas ◽

Monica Lestari Paramita ◽

Emma Barker ◽

Marcis Pinnis ◽

Ahmet Aker ◽

...

Keyword(s):

Evaluation Method ◽

Data Gathering ◽

Web Data ◽

Component System ◽

Terminology Extraction ◽

Domain Specific ◽

Term Extraction ◽

Main Components ◽

Multi Component System ◽

The Web

In this paper we make two contributions. First, we describe a multi-component system called BiTES (Bilingual Term Extraction System) designed to automatically gather domain-specific bilingual term pairs from Web data. BiTES components consist of data gathering tools, domain classifiers, monolingual text extraction systems and bilingual term aligners. BiTES is readily extendable to new language pairs and has been successfully used to gather bilingual terminology for 24 language pairs, including English and all official EU languages, save Irish. Second, we describe a novel set of methods for evaluating the main components of BiTES and present the results of our evaluation for six language pairs. Results show that the BiTES approach can be used to successfully harvest quality bilingual term pairs from the Web. Our evaluation method delivers significant insights about the strengths and weaknesses of our techniques. It can be straightforwardly reused to evaluate other bilingual term extraction systems and makes a novel contribution to the study of how to evaluate bilingual terminology extraction systems.

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Revisiting corpus creation and analysis tools for translation tasks

Cadernos de Tradução ◽

10.5007/2175-7968.2016v36nesp1p62 ◽

2016 ◽

Vol 36 (1) ◽

pp. 62 ◽

Cited By ~ 3

Author(s):

Claudio Fantinuoli

Keyword(s):

Contextual Information ◽

Analysis Tool ◽

Reliability Of Results ◽

High Quality ◽

Terminology Extraction ◽

Translation Quality ◽

Statistical Measures ◽

Corpus Creation ◽

Query System ◽

The Web

http://dx.doi.org/10.5007/2175-7968.2016v36nesp1p62Many translation scholars have proposed the use of corpora to allow professional translators to produce high quality texts which read like originals. Yet, the diffusion of this methodology has been modest, one reason being the fact that software for corpora analyses have been developed with the linguist in mind, which means that they are generally complex and cumbersome, offering many advanced features, but lacking the level of usability and the specific features that meet translators’ needs. To overcome this shortcoming, we have developed TranslatorBank, a free corpus creation and analysis tool designed for translation tasks. TranslatorBank supports the creation of specialized monolingual corpora from the web; it includes a concordancer with a query system similar to a search engine; it uses basic statistical measures to indicate the reliability of results; it accesses the original documents directly for more contextual information; it includes a statistical and linguistic terminology extraction utility to extract the relevant terminology of the domain and the typical collocations of a given term. Designed to be easy and intuitive to use, the tool may help translation students as well as professionals to increase their translation quality by adhering to the specific linguistic variety of the target text corpus.

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