We describe experiments carried out with adaptive language and translation models in the context of an interactive computer-assisted translation program. We developed cache-based ...
Laurent Nepveu, Guy Lapalme, Philippe Langlais, Ge...
Traditional word alignment approaches cannot come up with satisfactory results for Named Entities. In this paper, we propose a novel approach using a maximum entropy model for nam...
Broad-coverage repositories of semantic relations between verbs could benefit many NLP tasks. We present a semi-automatic method for extracting fine-grained semantic relations bet...
We address the issue of judging the significance of rare events as it typically arises in statistical naturallanguage processing. We first define a general approach to the problem...
This paper presents Domain Relevance Estimation (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respe...
We describe how simple, commonly understood statistical models, such as statistical dependency parsers, probabilistic context-free grammars, and word-to-word translation models, c...
This paper provides evidence for Genzel and Charniak's (2002) entropy rate principle, which predicts that the entropy of a sentence increases with its position in the text. W...
The morphology of Semitic languages is unique in the sense that the major word-formation mechanism is an inherently non-concatenative process of interdigitation, whereby two morph...
Accurate dependency recovery has recently been reported for a number of wide-coverage statistical parsers using Combinatory Categorial Grammar (CCG). However, overall figures give...