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KDD
2004
ACM
141views Data Mining» more  KDD 2004»
14 years 5 months ago
A Maximum Entropy Approach to Biomedical Named Entity Recognition
Machine learning approaches are frequently used to solve name entity (NE) recognition (NER). In this paper we propose a hybrid method that uses maximum entropy (ME) as the underly...
Yi-Feng Lin, Tzong-Han Tsai, Wen-Chi Chou, Kuen-Pi...
ACL
2006
13 years 6 months ago
Trimming CFG Parse Trees for Sentence Compression Using Machine Learning Approaches
Sentence compression is a task of creating a short grammatical sentence by removing extraneous words or phrases from an original sentence while preserving its meaning. Existing me...
Yuya Unno, Takashi Ninomiya, Yusuke Miyao, Jun-ich...
ANLP
2000
157views more  ANLP 2000»
13 years 6 months ago
Experiments on Sentence Boundary Detection
This paper explores the problem of identifying sentence boundaries in the transcriptions produced by automatic speech recognition systems. An experiment which determines the level...
Mark Stevenson, Robert J. Gaizauskas
IICAI
2007
13 years 6 months ago
ANERsys 2.0: Conquering the NER Task for the Arabic Language by Combining the Maximum Entropy with POS-tag Information
In this paper we describe an improved version of ANERsys, an Arabic Named Entity Recognition system for open-domain texts. The first version of ANERsys was totally based on the Ma...
Yassine Benajiba, Paolo Rosso
CICLING
2007
Springer
13 years 11 months ago
ANERsys: An Arabic Named Entity Recognition System Based on Maximum Entropy
Abstract. The task of Named Entity Recognition (NER) allows to identify proper names as well as temporal and numeric expressions, in an open-domain text. NER systems proved to be v...
Yassine Benajiba, Paolo Rosso, José-Miguel ...