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Using machine learning to perform automatic term recognition
Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
2010 (English)In: Proceedings of the LREC 2010 Workshop on Methods for automatic acquisition of Language Resources and their evaluation methods / [ed] Núria Bel, Béatrice Daille, Andrejs Vasiljevs, European Language Resources Association, 2010, 49-54 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper a machine learning approach is applied to Automatic Term Recognition (ATR). Similar approaches have been successfully used in Automatic Keyword Extraction (AKE). Using a dataset consisting of Swedish patent texts and validated terms belonging to these texts, unigrams and bigrams are extracted and annotated with linguistic and statistical feature values. Experiments using a varying ratio between positive and negative examples in the training data are conducted using the annotated n-grams. The results indicate that a machine learning approach is viable for ATR. Furthermore, a machine learning approach for bilingual ATR is discussed. Preliminary analysis however indicate that some modifications have to be made to apply the monolingual machine learning approach to a bilingual context.

Place, publisher, year, edition, pages
European Language Resources Association, 2010. 49-54 p.
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:liu:diva-75237ISI: 000356879501100ISBN: 978-2-9517408-6-0 (print)OAI: oai:DiVA.org:liu-75237DiVA: diva2:505121
Conference
LREC 2010 Workshop on Methods for automatic acquisition of Language Resources and their evaluation methods, 23 May 2010, Valletta, Malta
Available from: 2012-03-01 Created: 2012-02-22 Last updated: 2017-01-23Bibliographically approved
In thesis
1. Computational Terminology: Exploring Bilingual and Monolingual Term Extraction
Open this publication in new window or tab >>Computational Terminology: Exploring Bilingual and Monolingual Term Extraction
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Terminologies are becoming more important to modern day society as technology and science continue to grow at an accelerating rate in a globalized environment. Agreeing upon which terms should be used to represent which concepts and how those terms should be translated into different languages is important if we wish to be able to communicate with as little confusion and misunderstandings as possible.

Since the 1990s, an increasing amount of terminology research has been devoted to facilitating and augmenting terminology-related tasks by using computers and computational methods. One focus for this research is Automatic Term Extraction (ATE).

In this compilation thesis, studies on both bilingual and monolingual ATE are presented. First, two publications reporting on how bilingual ATE using the align-extract approach can be used to extract patent terms. The result in this case was 181,000 manually validated English-Swedish patent terms which were to be used in a machine translation system for patent documents. A critical component of the method used is the Q-value metric, presented in the third paper, which can be used to rank extracted term candidates (TC) in an order that correlates with TC precision. The use of Machine Learning (ML) in monolingual ATE is the topic of the two final contributions. The first ML-related publication shows that rule induction based ML can be used to generate linguistic term selection patterns, and in the second ML-related publication, contrastive n-gram language models are used in conjunction with SVM ML to improve the precision of term candidates selected using linguistic patterns.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 68 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1523
Keyword
terminology, automatic term extraction, automatic term recognition, computational terminology, terminology management
National Category
Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:liu:diva-75243 (URN)978-91-7519-944-3 (ISBN)
Presentation
2012-04-04, Alan Turing, Hus E, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2012-03-07 Created: 2012-02-23 Last updated: 2012-03-07Bibliographically approved

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Foo-2010-Using machine learning to perform automatic term recognition(454 kB)319 downloads
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