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Exploiting Diverse Knowledge Sources via Maximum Entropy in Named Entity Recognition
Andrew Borthwick
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John Sterling
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Eugene Agichtein
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Ralph Grishman
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Paper Details:
Year: 1998
Venue:
VLC |
WS |
SIG: SIGDAT
Citations
URL
Extracting the Names of Genes and Gene Products with a Hidden Markov Model
Nigel Collier
|
Chikashi Nobata
|
Jun-ichi Tsujii
|
Bunsetsu Identification Using Category-Exclusive Rules
Masaki Murata
|
Kiyotaka Uchimoto
|
Qing Ma
|
Hitoshi Isahara
|
Unsupervised Learning of Generalized Names
Roman Yangarber
|
Winston Lin
|
Ralph Grishman
|
Improving Mention Detection Robustness to Noisy Input
Radu Florian
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John Pitrelli
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Salim Roukos
|
Imed Zitouni
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Robust Named Entity Extraction from Large Spoken Archives
Benoît Favre
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Frédéric Béchet
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Pascal Nocéra
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Named Entity Recognition for South and South East Asian Languages: Taking Stock
Anil Kumar Singh
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Question Answering Using Maximum-Entropy Components
Abraham Ittycheriah
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Martin Franz
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Wei-Jing Zhu
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Adwait Ratnaparkhi
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Identifying and Tracking Entity Mentions in a Maximum Entropy Framework
Abraham Ittycheriah
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Lucian Lita
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Nanda Kambhatla
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Nicolas Nicolov
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Salim Roukos
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Margo Stys
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A Statistical Model for Multilingual Entity Detection and Tracking
R Florian
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H Hassan
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A Ittycheriah
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H Jing
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N Kambhatla
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X Luo
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N Nicolov
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S Roukos
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Named Entity Extraction Based on A Maximum Entropy Model and Transformation Rules
Kiyotaka Uchimoto
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Qing Ma
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Masaki Murata
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Hiromi Ozaku
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Hitoshi Isahara
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New Ranking Algorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron
Michael Collins
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Nigel Duffy
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Ranking Algorithms for Named Entity Extraction: Boosting and the VotedPerceptron
Michael Collins
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Automatic Acquisition of Named Entity Tagged Corpus from World Wide Web
Joohui An
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Seungwoo Lee
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Gary Geunbae Lee
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Shallow Analysis Based Assessment of Syntactic Complexity for Automated Speech Scoring
Suma Bhat
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Huichao Xue
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Su-Youn Yoon
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A Local Detection Approach for Named Entity Recognition and Mention Detection
Mingbin Xu
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Hui Jiang
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Sedtawut Watcharawittayakul
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Comparison between Tagged Corpora for the Named Entity Task
Chikashi Nobata
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Nigel Collier
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Jun’ichi Tsujii
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Combining Outputs of Multiple Japanese Named Entity Chunkers by Stacking
Takehito Utsuro
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Manabu Sassano
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Kiyotaka Uchimoto
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SLINERC: The Sydney Language-Independent Named Entity Recogniser and Classifier
Jon Patrick
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Casey Whitelaw
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Robert Munro
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Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons
Andrew McCallum
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Wei Li
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Bio-Medical Entity Extraction using Support Vector Machines
Koichi Takeuchi
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Nigel Collier
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Multi-Language Named-Entity Recognition System based on HMM
Kuniko Saito
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Masaaki Nagata
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Applying Coreference to Improve Name Recognition
Heng Ji
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Ralph Grishman
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Syntactic parser combination for improved dependency analysis
Francis Brunet-Manquat
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Feature Engineering and Post-Processing for Temporal Expression Recognition Using Conditional Random Fields
Sisay Fissaha Adafre
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Maarten de Rijke
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Improving Parsing Accuracy by Combining Diverse Dependency Parsers
Daniel Zeman
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Zdeněk Žabokrtský
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Structured Databases of Named Entities from Bayesian Nonparametrics
Jacob Eisenstein
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Tae Yano
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William Cohen
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Noah Smith
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Eric Xing
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Coupling Knowledge-Based and Data-Driven Systems for Named Entity Recognition
Damien Nouvel
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Jean-Yves Antoine
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Nathalie Friburger
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Arnaud Soulet
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Relationship Extraction based on Category of Medical Concepts from Lexical Contexts
Anupam Mondal
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Dipankar Das
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Sivaji Bandyopadhyay
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A Decision Tree Method for Finding and Classifying Names in Japanese Texts
Satoshi Sekine
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Ralph Grishman
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Hiroyuki Shinnou
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Exploiting Diversity in Natural Language Processing: Combining Parsers
John C. Henderson
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Eric Brill
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Research in Information Extraction: 1996-98
Ralph Grishman
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Transforming Examples into Patterns for Information Extraction
Roman Yangarber
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Ralph Grishman
|
http://www.utexas.edu/world/
http://www.mnemonic.com/software/memt
Field Of Study
Task
Tagging
Named Entity Recognition
Language
English
Japanese
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