FALCON: Boosting Knowledge for Answer Engines

PDF Version Also Available for Download.

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.

Physical Description

10 p.

Creation Information

Harabagiu, Sanda M.; Moldovan, Dan I.; Paşca, Marius. 1974-; Mihalcea, Rada, 1974-; Surdeanu, Mihai; Bunescu, Răzvan et al. November 2000.

Context

This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 260 times . More information about this paper can be viewed below.

Who

People and organizations associated with either the creation of this paper or its content.

Authors

Provided By

UNT College of Engineering

The UNT College of Engineering strives to educate and train engineers and technologists who have the vision to recognize and solve the problems of society. The college comprises six degree-granting departments of instruction and research.

Contact Us

What

Descriptive information to help identify this paper. Follow the links below to find similar items on the Digital Library.

Degree Information

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.

Physical Description

10 p.

Notes

Abstract: This paper presents the features of FALCON, an answer engine that integrates different forms of syntactic, semantic and pragmatic knowledge for the goal of achieving better performance. The answer engine handles question reformulations, finds the expected answer type from a large hierarchy that incorporates the WordNet semantic net and extracts answers after performing unifications on the semantic forms of the question and its candidate answers. To rule out erroneous answers, it provides justification option, implemented as an abductive proof. In TREC-9, FALCON generated a score of 58% for short answers and 76% for long answers.

Source

  • Ninth Annual Text Retrieval Conference (TREC), 2000, Gaithersburg, Maryland, United States

Language

Item Type

Collections

This paper is part of the following collection of related materials.

UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

What responsibilities do I have when using this paper?

When

Dates and time periods associated with this paper.

Creation Date

  • November 2000

Added to The UNT Digital Library

  • April 13, 2012, 9:48 a.m.

Description Last Updated

  • March 27, 2014, 11:58 a.m.

Usage Statistics

When was this paper last used?

Yesterday: 0
Past 30 days: 3
Total Uses: 260

Interact With This Paper

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Harabagiu, Sanda M.; Moldovan, Dan I.; Paşca, Marius. 1974-; Mihalcea, Rada, 1974-; Surdeanu, Mihai; Bunescu, Răzvan et al. FALCON: Boosting Knowledge for Answer Engines, paper, November 2000; (digital.library.unt.edu/ark:/67531/metadc83296/: accessed November 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.