Silla Jr, Carlos N., Kaestner, Celso A.A., Freitas, Alex A. (2003) A non-linear topic detection method for text summarization using Wordnet. In: Nunes, Maria da Graça Volpes and Aluisio, S.M. and Oliveira, L.H.M. and Teles, J.A., eds. Proc. I Workshop em Tecnologia da Informacao e Linguagem Humana. . ICMC-USP, Brazil (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:13897)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
| Official URL: http://www.cs.kent.ac.uk/pubs/2003/1761 |
Abstract
This paper deals with the problem of automatic topic detection in text documents. The proposed method follows a non-linear approach. The method uses a simple clustering algorithm to group the semantically-related sentences.
The distance between two sentences is calculated based on the distance between all nouns that appear in the sentences. The distance between two nouns is calculated
using the Wordnet thesaurus. An automatic text summarization system using a topic strength method was used to compare the results achieved by the Text Tiling Algorithm and the proposed method. The obtained initial results shows that the proposed method is a promising approach.
| Item Type: | Conference or workshop item (UNSPECIFIED) |
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| Uncontrolled keywords: | text mining, text summarization, Wordnet |
| Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Depositing User: | Mark Wheadon |
| Date Deposited: | 24 Nov 2008 18:00 UTC |
| Last Modified: | 05 Nov 2024 09:47 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/13897 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Freitas, Alex A..
| Creator's ORCID: |
 https://orcid.org/0000-0001-9825-4700
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