Literature DB >> 17050571

Building an abbreviation dictionary using a term recognition approach.

Naoaki Okazaki1, Sophia Ananiadou.   

Abstract

MOTIVATION: Acronyms result from a highly productive type of term variation and trigger the need for an acronym dictionary to establish associations between acronyms and their expanded forms.
RESULTS: We propose a novel method for recognizing acronym definitions in a text collection. Assuming a word sequence co-occurring frequently with a parenthetical expression to be a potential expanded form, our method identifies acronym definitions in a similar manner to the statistical term recognition task. Applied to the whole MEDLINE (7 811 582 abstracts), the implemented system extracted 886 755 acronym candidates and recognized 300 954 expanded forms in reasonable time. Our method outperformed base-line systems, achieving 99% precision and 82-95% recall on our evaluation corpus that roughly emulates the whole MEDLINE. AVAILABILITY AND SUPPLEMENTARY INFORMATION: The implementations and supplementary information are available at our web site: http://www.chokkan.org/research/acromine/

Mesh:

Year:  2006        PMID: 17050571     DOI: 10.1093/bioinformatics/btl534

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  24 in total

1.  Enhancing acronym/abbreviation knowledge bases with semantic information.

Authors:  Manabu Torii; Hongfang Liu
Journal:  AMIA Annu Symp Proc       Date:  2007-10-11

2.  Word add-in for ontology recognition: semantic enrichment of scientific literature.

Authors:  J Lynn Fink; Pablo Fernicola; Rahul Chandran; Savas Parastatidis; Alex Wade; Oscar Naim; Gregory B Quinn; Philip E Bourne
Journal:  BMC Bioinformatics       Date:  2010-02-24       Impact factor: 3.169

3.  LINNAEUS: a species name identification system for biomedical literature.

Authors:  Martin Gerner; Goran Nenadic; Casey M Bergman
Journal:  BMC Bioinformatics       Date:  2010-02-11       Impact factor: 3.169

4.  PathText: a text mining integrator for biological pathway visualizations.

Authors:  Brian Kemper; Takuya Matsuzaki; Yukiko Matsuoka; Yoshimasa Tsuruoka; Hiroaki Kitano; Sophia Ananiadou; Jun'ichi Tsujii
Journal:  Bioinformatics       Date:  2010-06-15       Impact factor: 6.937

5.  Building a high-quality sense inventory for improved abbreviation disambiguation.

Authors:  Naoaki Okazaki; Sophia Ananiadou; Jun'ichi Tsujii
Journal:  Bioinformatics       Date:  2010-03-25       Impact factor: 6.937

6.  A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience.

Authors:  Matthew Shardlow; Meizhi Ju; Maolin Li; Christian O'Reilly; Elisabetta Iavarone; John McNaught; Sophia Ananiadou
Journal:  Neuroinformatics       Date:  2019-07

7.  MBA: a literature mining system for extracting biomedical abbreviations.

Authors:  Yun Xu; ZhiHao Wang; YiMing Lei; YuZhong Zhao; Yu Xue
Journal:  BMC Bioinformatics       Date:  2009-01-09       Impact factor: 3.169

8.  Mining experimental evidence of molecular function claims from the literature.

Authors:  Colleen E Crangle; J Michael Cherry; Eurie L Hong; Alex Zbyslaw
Journal:  Bioinformatics       Date:  2007-10-17       Impact factor: 6.937

9.  Enhancing filter-based parenthetic abbreviation extraction methods.

Authors:  Houcemeddine Turki; Mohamed Ali Hadj Taieb; Mohamed Ben Aouicha
Journal:  J Am Med Inform Assoc       Date:  2021-03-01       Impact factor: 4.497

Review 10.  Calling International Rescue: knowledge lost in literature and data landslide!

Authors:  Teresa K Attwood; Douglas B Kell; Philip McDermott; James Marsh; Steve R Pettifer; David Thorne
Journal:  Biochem J       Date:  2009-12-10       Impact factor: 3.857

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