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ICML
2000
IEEE
14 years 8 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
FSKD
2006
Springer
190views Fuzzy Logic» more  FSKD 2006»
13 years 11 months ago
A Maximum Entropy Model Based Answer Extraction for Chinese Question Answering
We regard answer extraction of Question Answering (QA) system as a classification problem, classifying answer candidate sentences into positive or negative. To confirm the feasibil...
Ang Sun, Minghu Jiang, Yanjun Ma
ICML
2003
IEEE
14 years 8 months ago
Learning Mixture Models with the Latent Maximum Entropy Principle
We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from ...
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin...
NLPRS
2001
Springer
14 years 11 days ago
Statistical Parsing of Dutch using Maximum Entropy Models with Feature Merging
In this project report we describe work in statistical parsing using the maximum entropy technique and the Alpino language analysis system for Dutch. A major difficulty in this d...
Tony Mullen, Rob Malouf, Gertjan van Noord
EMNLP
2008
13 years 9 months ago
Maximum Entropy based Rule Selection Model for Syntax-based Statistical Machine Translation
This paper proposes a novel maximum entropy based rule selection (MERS) model for syntax-based statistical machine translation (SMT). The MERS model combines local contextual info...
Qun Liu, Zhongjun He, Yang Liu, Shouxun Lin