Acronym extraction and disambiguation in large-scale organizational web pages

Feng, Shicong; Xiong, Yan; Yao, Conglei; Li-wei, Zheng; Liu, Wei

doi:10.1145/1645953.1646206

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…At the definition extraction step the majority of known algorithms construct an appropriate definition near the acronym position in the text [2][3][4][5][6][7][8][9][10][11][12]. In most cases authors use different heuristics to decide where to search for the definition and how to find it.…”

Section: Definition Extraction Approachesmentioning

confidence: 99%

“…In this case further acronym-definition pairs dictionary refinement is not needed. Other techniques construct acronym-definition dictionary with high recall, but in this case refinement is needed because of high rate of false positive entries in the resulting dictionary [2][3][4]7,[10][11][12]. We will follow the second approach to keep recall of acronymdefinition dictionary at this step.…”

Section: Definition Extraction Approachesmentioning

confidence: 99%

“…Unlike other algorithms, the unsupervised ranking algorithm enables the refinement of the acronym-definition dictionary in [3]. A method for acronyms disambiguation based on graph of links between pages is also described there.…”

Section: Dictionary Filtering Approachesmentioning

confidence: 99%

“…An acronym is usually introduced along with its definition when it is first mentioned in a document. Many approaches have been proposed for acronym extraction in the literature [2][3][4][5][6][7][8][9][10][11][12], all of them following a similar scheme: (1) candidate acronyms recognition, (2) definition extraction, (3) filtering of acronym-definition pairs (an optional step). The first and second steps usually exploit a set of hand-crafted rules or patterns, designed to match common formats of acronyms definitions.…”

Section: Introductionmentioning

confidence: 99%

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High-recall extraction of acronym-definition pairs with relevance feedback

Yarygina¹,

Vassilieva

2012

Proceedings of the 2012 Joint EDBT/ICDT Workshops

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This paper addresses the problem of extracting acronyms and their definitions from large documents in a setting, when high recall is required and user feedback is available. We propose a three step approach to deal with the problem. First, acronym candidates are extracted using a weak regular expression. This step results in a list of acronyms with high recall but low precision rates. Second, definitions are constructed for every acronym candidate from its surrounding text. And last, a classifier is used to select genuine acronymdefinition pairs. At the last step we use relevance feedback mechanism to tune the classifier model for every particular document. This allows achieving reasonable precision without losing recall. As opposed to existing approaches, either created to be generic and domain independent or tuned to one particular domain, our method is adaptive to an input document. We evaluate the proposed approach using three datasets from different domains. The experiments prove the validity of the presented ideas.

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Section: Definition Extraction Approachesmentioning

confidence: 99%

Section: Definition Extraction Approachesmentioning

confidence: 99%

Section: Dictionary Filtering Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-recall extraction of acronym-definition pairs with relevance feedback

Yarygina¹,

Vassilieva

2012

Proceedings of the 2012 Joint EDBT/ICDT Workshops

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“…Recently, many works [Park and Byrd 2001;Pakhomov 2002;Okazaki and Ananiadou 2006;Feng et al 2009] have been done on the identification of English abbreviations. In English, abbreviations are constructed in a simple manner.…”

Section: Introductionmentioning

confidence: 99%

Extracting Chinese abbreviation-definition pairs from anchor texts

Xie

Zheng

Liu

et al. 2011

2011 International Conference on Machine Learning and Cybernetics

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This paper proposes an automatic scheme to extractChinese abbreviations and their corresponding definitions from large-scale anchor texts. This method is motivated by the observation that the more frequently two anchor texts point to the same web page, the more related they are. Since abbreviation-definition pairs are highly related, they can be extracted from these related words. Our method involves three steps. Firstly we utilize external statistical features to extract candidate abbreviation-definition pairs from anchor texts. Secondly we extract internal features from candidate pairs and adopt Conditional Random Fields (CRFs) to compute a score for each candidate pair. Finally we combine external and internal features to generate the final pairs. Experimental results show that this method can accurately extract Chinese abbreviation-definition pairs from anchor texts and combining both external and internal features is effective for extracting abbreviation-definition pairs.

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Discovery, Enrichment and Disambiguation of Acronyms

Barua

Patel

2016

Big Data Analytics and Knowledge Discovery

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Acronym extraction and disambiguation in large-scale organizational web pages

Cited by 8 publications

References 6 publications

High-recall extraction of acronym-definition pairs with relevance feedback

High-recall extraction of acronym-definition pairs with relevance feedback

Extracting Chinese abbreviation-definition pairs from anchor texts

Discovery, Enrichment and Disambiguation of Acronyms

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