Several methods are known for parsing languages generated by Tree Adjoining Grammars (TAGs) in O(n 6) worst case running time. In this paper we investigate which restrictions on T...
This paper proposes a learning method of translation rules from parallel corpora. This method applies the maximum entropy principle to a probabilistic model of translation rules. ...
Much of the power of probabilistic methods in modelling language comes from their ability to compare several derivations for the same string in the language. An important starting...
We propose a comprehensive theory of codemixed discourse, encompassing equivalencepoint and insertional code-switching, palindromic constructions and lexical borrowing. The starti...
An efficient use of lexical cohesion is described for ranking text units according to their contribution in defining the meaning of a text (textual saliency), their ability to for...
In this paper, we provide an account of how to generate sentences with coordination constructions from clause-sized semantic representations. An algorithm is developed and various...
For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract valu...
We present an architecture and an on-line learning algorithm and apply it to the problem of part-ofspeech tagging. The architecture presented, SNOW, is a network of linear separat...
We discuss an interactive approach to robust interpretation in a large scale speech-to-speech translation system. Where other interactive approaches to robust interpretation have ...
The probabilistic relation between verbs and their arguments plays an important role in modern statistical parsers and supertaggers, and in psychological theories of language proc...