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» Improving Language Models by Clustering Training Sentences
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EMNLP
2007
13 years 9 months ago
Bootstrapping Feature-Rich Dependency Parsers with Entropic Priors
One may need to build a statistical parser for a new language, using only a very small labeled treebank together with raw text. We argue that bootstrapping a parser is most promis...
David A. Smith, Jason Eisner
EMNLP
2008
13 years 9 months ago
Lattice-based Minimum Error Rate Training for Statistical Machine Translation
Minimum Error Rate Training (MERT) is an effective means to estimate the feature function weights of a linear model such that an automated evaluation criterion for measuring syste...
Wolfgang Macherey, Franz Josef Och, Ignacio Thayer...
EMNLP
2004
13 years 9 months ago
Dependencies vs. Constituents for Tree-Based Alignment
Given a parallel parsed corpus, statistical treeto-tree alignment attempts to match nodes in the syntactic trees for a given sentence in two languages. We train a probabilistic tr...
Daniel Gildea
CIKM
2008
Springer
13 years 10 months ago
Integrating clustering and multi-document summarization to improve document understanding
Document understanding techniques such as document clustering and multi-document summarization have been receiving much attention in recent years. Current document clustering meth...
Dingding Wang, Shenghuo Zhu, Tao Li, Yun Chi, Yiho...
EMNLP
2004
13 years 9 months ago
Monolingual Machine Translation for Paraphrase Generation
We apply statistical machine translation (SMT) tools to generate novel paraphrases of input sentences in the same language. The system is trained on large volumes of sentence pair...
Chris Quirk, Chris Brockett, William B. Dolan