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ICML
2005
IEEE
16 years 4 months ago
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...
NAR
2006
82views more  NAR 2006»
15 years 4 months ago
PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins
PROFtmb predicts transmembrane beta-barrel (TMB) proteins in Gram-negative bacteria. For each query protein, PROFtmb provides both a Z-value indicating that the protein actually c...
Henry R. Bigelow, Burkhard Rost
ACL
2009
15 years 1 months ago
Part of Speech Tagger for Assamese Text
Assamese is a morphologically rich, agglutinative and relatively free word order Indic language. Although spoken by nearly 30 million people, very little computational linguistic ...
Navanath Saharia, Dhrubajyoti Das, Utpal Sharma, J...
CORR
2011
Springer
191views Education» more  CORR 2011»
14 years 11 months ago
A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels
We propose a factor-graph-based approach to joint channel-estimationand-decoding of bit-interleaved coded orthogonal frequency division multiplexing (BICM-OFDM). In contrast to ex...
Philip Schniter
ICML
2000
IEEE
16 years 4 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...