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Semi-Supervised Maximum Entropy Based Approach to Acronym and Abbreviation Normalization in Medical Texts
Serguei Pakhomov
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Paper Details:
Month: July
Year: 2002
Location: Philadelphia, Pennsylvania, USA
Venue:
ACL |
Citations
URL
A Discriminative Alignment Model for Abbreviation Recognition
Naoaki Okazaki
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Sophia Ananiadou
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Jun’ichi Tsujii
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Mining and Modeling Relations between Formal and Informal Chinese Phrases from Web Corpora
Zhifei Li
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David Yarowsky
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Bootstrapped Named Entity Recognition for Product Attribute Extraction
Duangmanee Putthividhya
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Junling Hu
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Using Maximum Entropy to Extract Biomedical Named Entities without Dictionaries
Tzong-Han Tsai
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Chia-Wei Wu
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Wen-Lian Hsu
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A Discriminative Approach to Japanese Abbreviation Extraction
Naoaki Okazaki
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Mitsuru Ishizuka
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Jun’ichi Tsujii
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High Throughput Modularized NLP System for Clinical Text
Serguei Pakhomov
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James Buntrock
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Patrick Duffy
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A Term Recognition Approach to Acronym Recognition
Naoaki Okazaki
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Sophia Ananiadou
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Combined One Sense Disambiguation of Abbreviations
Yaakov HaCohen-Kerner
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Ariel Kass
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Ariel Peretz
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Robust Approach to Abbreviating Terms: A Discriminative Latent Variable Model with Global Information
Xu Sun
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Naoaki Okazaki
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Jun’ichi Tsujii
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Disambiguation of Biomedical Abbreviations
Mark Stevenson
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Yikun Guo
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Abdulaziz Alamri
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Robert Gaizauskas
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Using Second-order Vectors in a Knowledge-based Method for Acronym Disambiguation
Bridget T. McInnes
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Ted Pedersen
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Ying Liu
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Serguei V. Pakhomov
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Genevieve B. Melton
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http://maxent.sourceforge.net
http://maxent.sourceforge.net
Field Of Study
Linguistic Trends
Discourse
Task
Tagging
Word Sense Disambiguation
Information Retrieval
Biomedical
Approach
Unsupervised Learning
Language
English
Dataset
Clinical Notes
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