Recognizing acronyms and their definitions

Taghva, Kazem; Gilbreth, Jeff

doi:10.1007/s100320050018

Cited by 82 publications

(59 citation statements)

References 7 publications

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“…There is also a similarity between the current work and work reported in Taghva and Gilbreth (1995) on using approximate string matching methods to induce interpretations of acronyms or letter sequences from their full word expansions found somewhere in the immediate context of the given letter sequence. Thus, from a text such as today, leaders of the North Atlantic Treaty Organization (NATO) met in Brussels .…”

Section: Discussionmentioning

confidence: 80%

Normalization of non-standard words

Sproat

Black

Chen³

et al. 2001

Computer Speech & Language

256

167

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Section: Discussionmentioning

confidence: 80%

Normalization of non-standard words

Sproat

Black

Chen³

et al. 2001

Computer Speech & Language

256

167

View full text Add to dashboard Cite

“…One of the earliest acronym identification systems (Taghva and Gilbreth, 1999) is AFP (Acronym Finding Program). The AFP system first identifies candidate acronyms, which the authors define as uppercase words of three to ten letters.…”

Section: Related Workmentioning

confidence: 99%

A Supervised Learning Approach to Acronym Identification

Nadeau

Turney

2005

Lecture Notes in Computer Science

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Abstract. This paper addresses the task of finding acronym-definition pairs in text. Most of the previous work on the topic is about systems that involve manually generated rules or regular expressions. In this paper, we present a supervised learning approach to the acronym identification task. Our approach reduces the search space of the supervised learning system by putting some weak constraints on the kinds of acronym-definition pairs that can be identified. We obtain results comparable to hand-crafted systems that use stronger constraints. We describe our method for reducing the search space, the features used by our supervised learning system, and our experiments with various learning schemes.

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“…The task is to identify the 'authentic' long-form in the textual fragment if any. Existing methods for solving this problem can be categorized into three groups: using heuristics and/or scoring rules (Adar, 2004;Ao et al, 2005;Schwartz et al, 2003;Taghva et al, 1999;Wren et al, 2002;Yu et al, 2002); machine learning (Chang et al, 2006;Pakhomov, 2002;Nadeau et al, 2005); and statistics (Hisamitsu et al, 2001;Liu et al, 2003).…”

Section: Related Workmentioning

confidence: 99%

Building an abbreviation dictionary using a term recognition approach

Okazaki

Ananiadou

2006

Bioinformatics

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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:

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Recognizing acronyms and their definitions

Cited by 82 publications

References 7 publications

Normalization of non-standard words

Normalization of non-standard words

A Supervised Learning Approach to Acronym Identification

Building an abbreviation dictionary using a term recognition approach

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