In this paper, we describe the pronominal anaphora resolution module of Lucy, a portable English understanding system. The design of this mo;clule was motivated by the observation that, although there exist many theories of anaphora resolution, no one of these theories is complete. Thus we have implemented a blackboard-like architecture in which individual partial theories can be encoded as separate modules that can interact to propose candidate antecedents and to evaluate each other's proposals.
The three-tiered discourse representation defined in (Luperfoy, 1991) is applied to multimodal humancomputer interface (HCI) dialogues. In the applied system the three tiers are (1) a linguistic analysis (morphological, syntactic, sentential semantic) of input and output communicative events including keyboard-entered command language atoms, NL strings, mouse clicks, output text strings, and output graphical events; (2) a discourse model representation
This paper details a software architecture for discourse processing in spoken dialogue systems, where the three component tasks of discourse processing are (1) Dialogue Management, (2) Context Tracking, and (3) Pragmatic Adaptation. We define these three component tasks and describe their roles in a complex, near-future scenario in which multiple humans interact with each other and with computers in multiple, simultaneous dialogue exchanges. This paper reports on the software modules that accomplish the three component tasks of discourse processing, and an architecture for the interaction among these modules and with other modules of the spoken dialogue system. A motivation of this work is reusable discourse processing software for integration with non-discourse modules in spoken dialogue systems. We document the use of this architecture and its components in several prototypes, and also discuss its potential application to spoken dialogue systems defined in the near-future scenario.
This paper presents a multi-neuro tagger that uses variable lengths of contexts and weighted inputs (with information gains) for part of speech tagging. Computer experiments show that it has a correct rate of over 94% for tagging ambiguous words when a small Thai corpus with 22,311 ambiguous words is used for training. This result is better than any of the results obtained using the single-neuro taggers with fixed but different lengths of contexts, which indicates that the multi-neuro tagger can dynamically find a suitable length of contexts in tagging.
This paper details a software architecture for discourse processing in spoken dialogue systems, where the three component tasks of discourse processing are (1) Dialogue Management, (2) Context Tracking, and (3) Pragmatic Adaptation. We define these three component tasks and describe their roles in a complex, near-future scenario in which multiple humans interact with each other and with computers in multiple, simultaneous dialogue exchanges. This paper reports on the software modules that accomplish the three component tasks of discourse processing, and an architecture for the interaction among these modules and with other modules of the spoken dialogue system. A motivation of this work is reusable discourse processing software for integration with non-discourse modules in spoken dialogue systems. We document the use of this architecture and its components in several prototypes, and also discuss its potential application to spoken dialogue systems defined in the near-future scenario.
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