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 proposes an approach to improve graph-based dependency parsing by using decision history. We introduce a mechanism that considers short dependencies computed in the ear...
The goal of our research is to improve event extraction by learning to identify secondary role filler contexts in the absence of event keywords. We propose a multilayered event e...
Via an oracle experiment, we show that the upper bound on accuracy of a CCG parser is significantly lowered when its search space is pruned using a supertagger, though the supert...
Syntactic analysis of search queries is important for a variety of information-retrieval tasks; however, the lack of annotated data makes training query analysis models difficult...
Kuzman Ganchev, Keith Hall, Ryan T. McDonald, Slav...