Open AccessProceedings Article
Automatic Term Recognition Needs Multiple Evidence
Natalia V. Loukachevitch
- 01 May 2012
- pp 2401-2407
TL;DR: It is argued that the automatic term extraction procedure is an inherently multifactor process and the term extraction models needs to be based on multiple features including a specific type of a terminological resource under development.
read more
Abstract: In this paper we argue that the automatic term extraction procedure is an inherently multifactor process and the term extraction models needs to be based on multiple features including a specific type of a terminological resource under development. We proposed to use three types of features for extraction of two-word terms and showed that all these types of features are useful for term extraction. The set of features includes new features such as features extracted from an existing domain-specific thesaurus and features based on Internet search results. We studied the set of features for term extraction in two different domains and showed that the combination of several types of features considerably enhances the quality of the term extraction procedure. We found that for developing term extraction models in a specific domain, it is important to take into account some properties of the domain.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Proceedings Article
A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set
Merley da Silva Conrado,Thiago Alexandre Salgueiro Pardo,Solange Oliveira Rezende +2 more
- 01 Jun 2013
TL;DR: An automatic term extraction approach that uses machine learning incorporating varied and rich features of candidate terms and achieving state of the art results for unigram extraction in Brazilian Portuguese is proposed.
TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset
Ayla Rigouts Terryn,Veronique Hoste,Patrick Drouin,Els Lefever +3 more
- 01 May 2020
TL;DR: The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length.
33
SemRe-Rank: Improving Automatic Term Extraction by Incorporating Semantic Relatedness with Personalised PageRank
TL;DR: SemRe-Rank is introduced, the first method based on this principle, to incorporate semantic relatedness—an often overlooked venue—into an existing ATE method to further improve its performance, and has achieved widespread improvement over all base methods and across all datasets.
Topic models can improve domain term extraction
Elena I. Bolshakova,Natalia V. Loukachevitch,Michael Nokel +2 more
- 24 Mar 2013
TL;DR: It is demonstrated that topic information improves the quality of term extraction, as well as NMF with KL-divergence minimization is the best among the models under study.
29
•Posted Content
SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRank.
TL;DR: SemRe-Rank is introduced, the first method based on this principle, to incorporate semantic relatedness - an often overlooked venue - into an existing ATE method to further improve its performance, and is shown to have achieved widespread improvement over all base methods and across all datasets.
16
References
•Book
Introduction to Information Retrieval
Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze +2 more
- 01 Jan 2008
TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
Word sense disambiguation: A survey
TL;DR: This work introduces the reader to the motivations for solving the ambiguity of words and provides a description of the task, and overviews supervised, unsupervised, and knowledge-based approaches.
Methods of automatic term recognition : a review
Kyo Kageura,Bin Umino +1 more
TL;DR: This paper tries to give an overview of the principles and methods of automatic term recognition and two major trends are examined, i.e., studies in automatic recognition of significant elements for indexing mainly carried out in information-retrieval circles and current research in automaticterm recognition in the field of computational linguistics.
440
•Proceedings Article
BootCaT: Bootstrapping corpora and terms from the web
Marco Baroni,Silvia Bernardini +1 more
- 01 May 2004
TL;DR: The BootCaT toolkit, a suite of perl programs implementing an iterative procedure to bootstrap specialized corpora and terms from the web, is introduced and an evaluation of the tools is conducted by applying them to the construction of English and Italian Corpora and term lists from the domain of psychiatry.
•Proceedings Article
A Comparative Evaluation of Term Recognition Algorithms
Ziqi Zhang,José Iria,Christopher Brewster,Fabio Ciravegna +3 more
- 01 May 2008
TL;DR: This paper evaluated the six approaches using two different corpora and showed how the voting algorithm performs best on one corpus and less well using the Genia corpus, indicating that choice and design of corpus has a major impact on the evaluation of term recognition algorithms.