Abbreviation recognition with MaxEnt model

Chunyu Kit; Xiaoyue Liu; Jonathan J. Webster

doi:10.1007/11671299_14

Abbreviation recognition with MaxEnt model

Chunyu Kit
, Xiaoyue Liu
, Jonathan J. Webster

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Abbreviated words carry critical information in the literature of many special domains. This paper reports our research in recognizing dotted abbreviations with MaxEnt model. The key points in our work include: (1) allowing the model to optimize with as many features as possible to capture the text characteristics of context words, and (2) utilizing simple lexical information such as sentence-initial words and candidate word length for performance enhancement. Experimental results show that this approach achieves impressive performance on the WSJ corpus. © Springer-Verlag Berlin Heidelberg 2006.

Original language	English
Title of host publication	Computational Linguistics and Intelligent Text Processing
Subtitle of host publication	7th International Conference, CICLing 2006, Proceedings
Publisher	Springer Verlag
Pages	117-120
Volume	3878 LNCS
ISBN (Print)	3540322051, 9783540322054
DOIs	https://doi.org/10.1007/11671299_14
Publication status	Published - 2006
Event	7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006 - Mexico City, Mexico Duration: 19 Feb 2006 → 25 Feb 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3878 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006
Place	Mexico
City	Mexico City
Period	19/02/06 → 25/02/06

Access Output

10.1007/11671299_14

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

Kit, C., Liu, X., & Webster, J. J. (2006). Abbreviation recognition with MaxEnt model. In Computational Linguistics and Intelligent Text Processing: 7th International Conference, CICLing 2006, Proceedings (Vol. 3878 LNCS, pp. 117-120). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3878 LNCS). Springer Verlag. https://doi.org/10.1007/11671299_14

Kit, Chunyu ; Liu, Xiaoyue ; Webster, Jonathan J. / Abbreviation recognition with MaxEnt model. Computational Linguistics and Intelligent Text Processing: 7th International Conference, CICLing 2006, Proceedings. Vol. 3878 LNCS Springer Verlag, 2006. pp. 117-120 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ce57966435d04b9198a06b1abe35296e,

title = "Abbreviation recognition with MaxEnt model",

abstract = "Abbreviated words carry critical information in the literature of many special domains. This paper reports our research in recognizing dotted abbreviations with MaxEnt model. The key points in our work include: (1) allowing the model to optimize with as many features as possible to capture the text characteristics of context words, and (2) utilizing simple lexical information such as sentence-initial words and candidate word length for performance enhancement. Experimental results show that this approach achieves impressive performance on the WSJ corpus. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.",

author = "Chunyu Kit and Xiaoyue Liu and Webster, \{Jonathan J.\}",

year = "2006",

doi = "10.1007/11671299\_14",

language = "English",

isbn = "3540322051",

volume = "3878 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "117--120",

booktitle = "Computational Linguistics and Intelligent Text Processing",

address = "Germany",

note = "7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006 ; Conference date: 19-02-2006 Through 25-02-2006",

}

Kit, C, Liu, X & Webster, JJ 2006, Abbreviation recognition with MaxEnt model. in Computational Linguistics and Intelligent Text Processing: 7th International Conference, CICLing 2006, Proceedings. vol. 3878 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3878 LNCS, Springer Verlag, pp. 117-120, 7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006, Mexico City, Mexico, 19/02/06. https://doi.org/10.1007/11671299_14

Abbreviation recognition with MaxEnt model. / Kit, Chunyu; Liu, Xiaoyue; Webster, Jonathan J.
Computational Linguistics and Intelligent Text Processing: 7th International Conference, CICLing 2006, Proceedings. Vol. 3878 LNCS Springer Verlag, 2006. p. 117-120 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3878 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Abbreviation recognition with MaxEnt model

AU - Kit, Chunyu

AU - Liu, Xiaoyue

AU - Webster, Jonathan J.

PY - 2006

Y1 - 2006

N2 - Abbreviated words carry critical information in the literature of many special domains. This paper reports our research in recognizing dotted abbreviations with MaxEnt model. The key points in our work include: (1) allowing the model to optimize with as many features as possible to capture the text characteristics of context words, and (2) utilizing simple lexical information such as sentence-initial words and candidate word length for performance enhancement. Experimental results show that this approach achieves impressive performance on the WSJ corpus. © Springer-Verlag Berlin Heidelberg 2006.

AB - Abbreviated words carry critical information in the literature of many special domains. This paper reports our research in recognizing dotted abbreviations with MaxEnt model. The key points in our work include: (1) allowing the model to optimize with as many features as possible to capture the text characteristics of context words, and (2) utilizing simple lexical information such as sentence-initial words and candidate word length for performance enhancement. Experimental results show that this approach achieves impressive performance on the WSJ corpus. © Springer-Verlag Berlin Heidelberg 2006.

UR - https://www.scopus.com/pages/publications/33745570105

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33745570105&origin=recordpage

U2 - 10.1007/11671299_14

DO - 10.1007/11671299_14

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 3540322051

SN - 9783540322054

VL - 3878 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 117

EP - 120

BT - Computational Linguistics and Intelligent Text Processing

PB - Springer Verlag

T2 - 7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006

Y2 - 19 February 2006 through 25 February 2006

ER -

Kit C, Liu X, Webster JJ. Abbreviation recognition with MaxEnt model. In Computational Linguistics and Intelligent Text Processing: 7th International Conference, CICLing 2006, Proceedings. Vol. 3878 LNCS. Springer Verlag. 2006. p. 117-120. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11671299_14

Abbreviation recognition with MaxEnt model

Abstract

Publication series

Conference

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this