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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 ...
139
Voted
ML
1998
ACM
139views Machine Learning» more  ML 1998»
15 years 2 months ago
The Hierarchical Hidden Markov Model: Analysis and Applications
We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our m...
Shai Fine, Yoram Singer, Naftali Tishby
100
Voted
ICML
2006
IEEE
16 years 4 months ago
Topic modeling: beyond bag-of-words
Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assu...
Hanna M. Wallach
ICML
2004
IEEE
16 years 4 months ago
The multiple multiplicative factor model for collaborative filtering
We describe a class of causal, discrete latent variable models called Multiple Multiplicative Factor models (MMFs). A data vector is represented in the latent space as a vector of...
Benjamin M. Marlin, Richard S. Zemel
AI
2009
Springer
15 years 6 months ago
Financial Forecasting Using Character N-Gram Analysis and Readability Scores of Annual Reports
Abstract. Two novel Natural Language Processing (NLP) classification techniques are applied to the analysis of corporate annual reports in the task of financial forecasting. The ...
Matthew Butler, Vlado Keselj