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ASKNet: Creating and Evaluating Large Scale Integrated Semantic Networks

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Extracting semantic information from multiple natural language sources and combining that information into a single unified resource is an important and fundamental goal for natural language processing. Large scale resources of this kind can be useful for a wide variety of tasks including question answering, word sense disambiguation and knowledge discovery. A single resource representing the information in multiple documents can provide significantly more semantic information than is available from the documents considered independently. In this talk I will describe the ASK Net system, which extracts semantic information from a large number of English texts, and combines that information into a large scale semantic network using spreading activation based techniques. I will discuss how the networks are created and integrated, and also touch on the topics of evaluation and some potential future uses for the ASK Net system.

This talk is part of the NLIP Seminar Series series.

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