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Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling , a simple Monte Carlo method used to perform approximate(More)
The area of learning in multi-agent systems is today one of the most fertile grounds for interaction between game theory and artificial intelligence. We focus on the foundational questions in this interdisciplinary area, and identify several distinct agendas that ought to, we argue, be separated. The goal of this article is to start a discussion in the(More)
The applicability of many current information extraction techniques is severely limited by the need for supervised training data. We demonstrate that for certain field structured extraction tasks, such as classified advertisements and bibliographic citations , small amounts of prior knowledge can be used to learn effective models in a primarily(More)
This paper proposes a new architecture for tex-tual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally(More)
This paper advocates a new architecture for tex-tual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally(More)
We describe an approach to textual inference that improves alignments at both the typed dependency level and at a deeper semantic level. We present a machine learning approach to alignment scoring, a stochas-tic search procedure, and a new tool that finds deeper semantic alignments, allowing rapid development of semantic features over the aligned graphs.(More)
Dispersion games are the generalization of the <i>anticoordination game</i> to arbitrary numbers of agents and actions. In these games agents prefer outcomes in which the agents are maximally dispersed over the set of possible actions. This class of games models a large number of natural problems, including load balancing in computer(More)
This paper presents our work on textual inference and situates it within the context of the larger goals of machine reading. The textual inference task is to determine if the meaning of one text can be inferred from the meaning of another and from background knowledge. Our system generates semantic graphs as a representation of the meaning of a text. This(More)