This work extends a semi-automatic grammar induction approach previously proposed in [1]. We investigate the use of Information Gain (IG) in place of Mutual Information (MI) for g...
We present an approach to grammar induction that utilizes syntactic universals to improve dependency parsing across a range of languages. Our method uses a single set of manually-...
Tahira Naseem, Harr Chen, Regina Barzilay, Mark Jo...
We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates...
Ezra Black, Frederick Jelinek, John D. Lafferty, D...
The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts ...
Porting a Natural Language Processing (NLP) system to a new domain remains one of the bottlenecks in syntactic parsing, because of the amount of effort required to fix gaps in the...