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FALCON: Boosting Knowledge for Answer Engines

Description: This paper discusses FALCON, an answer engine that integrates different forms of syntactic, semantic and pragmatic knowledge for the goal of achieving better performance.
Date: November 2000
Creator: Harabagiu, Sanda M.; Moldovan, Dan I.; Paşca, Marius. 1974-; Mihalcea, Rada, 1974-; Surdeanu, Mihai; Bunescu, Răzvan et al.
Partner: UNT College of Engineering

A general purpose semantic parser using FrameNet and WordNet®.

Description: Syntactic parsing is one of the best understood language processing applications. Since language and grammar have been formally defined, it is easy for computers to parse the syntactic structure of natural language text. Does meaning have structure as well? If it has, how can we analyze the structure? Previous systems rely on a one-to-one correspondence between syntactic rules and semantic rules. But such systems can only be applied to limited fragments of English. In this thesis, we propose a general-purpose shallow semantic parser which utilizes a semantic network (WordNet), and a frame dataset (FrameNet). Semantic relations recognized by the parser are based on how human beings represent knowledge of the world. Parsing semantic structure allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.
Date: May 2004
Creator: Shi, Lei
Partner: UNT Libraries