Learn More
Centering theory is the best-known framework for theorizing about local coherence and salience; however, its claims are articulated in terms of notions which are only partially specified, such as " utterance, " " realization, " or " ranking. " A great deal of research has attempted to arrive at more detailed specifications of these parameters of the theory;(More)
Based on an ongoing attempt to integrate Natural Language instructions with human figure animation, we demonstrate that agents' understanding and use of instructions can complement what they can derive from the environment in which they act. We focus on two attitudes that contribute to agents' behavior-their intentions and their expectations-and show how(More)
This chapter explores the correlation between centering and different forms of pronominal reference in Italian, in particular zeros and overt pronouns in subject position. In previous work (Di Eugenio, 1990), I proposed that such alternation could be explained in terms of centering transitions. In this chapter, I verify those hypotheses by means of a small(More)
This paper presents a first-order logic learning approach to determine rhetorical relations between discourse segments. Beyond linguistic cues and lexical information, our approach exploits compositional semantics and segment discourse structure data. We report a statistically significant improvement in classifying relations over attribute-value learning(More)
In this paper we will present our ongoing work on a plan-based discourse processor developed in the context of the Enthusiast Spanish to English translation system as part of the JANUS multilingual speech-to-speech translation system. We will demonstrate that theories of discourse which postulate a strict tree structure of discourse on either the(More)
This paper presents an approach for representing queries in natural language as semantic networks. The semantic representation is intended to facilitate the translation between natural language and database queries. A domain agnostic algorithm, based on shallow features, is used to map a sentence to a sub-network of concepts within a larger ontology. This(More)
We discuss Feature Latent Semantic Analysis (FLSA), an extension to Latent Semantic Analysis (LSA). LSA is a statistical method that is ordinarily trained on words only; FLSA adds to LSA the richness of the many other linguistic features that a corpus may be labeled with. We applied FLSA to dialogue act classification with excellent results. We report(More)