Traditional vector-based models use word co-occurrence counts from large corpora to represent lexical meaning. In this paper we present a novel approach for constructing semantic ...
We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
This paper describes Mo’K, a configurable workbench that supports the development of conceptual clustering methods for ontology building. Mo’K is intended to assist ontology de...
In data-oriented language processing, an annotated language corpus is used as a stochastic grammar. The most probable analysis of a new sentence is constructed by combining fragme...
We present a technique for automatic induction of slot annotations for subcategorization frames, based on induction of hidden classes in the EM framework of statistical estimation...
Mats Rooth, Stefan Riezler, Detlef Prescher, Glenn...