Identifying multidocument relations

Proceedings of the 7th International Workshop on Natural Language Processing and Cognitive Science, NLPCS 2010, in Conjunction with ICEIS 2010

Volumn , Issue , 2010, Pages 60-69

  Maziero, Erick Galani ^a   Del Rosario Castro Jorge, Maria Lucía ^a   Pardo, Thiago Alexandre Salgueiro ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC IDENTIFICATION; CROSS-DOCUMENT STRUCTURE THEORIES; DIGITAL WORLD; MACHINE-LEARNING; MULTI-DOCUMENT; MULTI-DOCUMENT SUMMARIZATION; QUESTION ANSWERING;

AUTOMATION; COMPUTATIONAL LINGUISTICS; LEARNING ALGORITHMS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 78649341997     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Afantenos, S. D.; Doura, I.; Kapellou, E.; Karkaletsis, V. (2004). Exploiting Cross-Document Relations for Multi-document Evolving Summarization. In the Proceedings of SETN, pp. 410-419.
2. Aleixo, P. e Pardo, T. A. S. (2008). CSTNews: Urn Corpus de Textos Jornalisticos Anotados segundo a Teoria Discursiva Multidocumento CST (Cross-document Structure Theory). Série de Relatórios Técnicos do Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, no. 326. São Carlos-SP, Maio, 12p.
3. Aleixo, P. e Pardo, T. A. S. (2008). CSTTool: Uma Ferramenta Semi-automática para Anotação de Córpus pela Teoria Discursiva Multidocumento CST Série de Relatórios Técnicos do Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, no. 321. São Carlos-SP, Maio, 11p.
4. Aleixo, P. and Pardo, T. A. S. (2008). Finding Related Sentences in Multiple Documents for Multidocument Discourse Parsing of Brazilian Portuguese Texts. In Anais do VI Workshop em Tecnologia da Informação e da Linguagem Humana - TIL, pp. 298-303.
5. Allan, J.; Carbonell, J.; Doddington, G.; Yamron, J.; Yang, Y. (1998). Topic detection and tracking pilot study: final report. In the Proceedings of the DARPA Broadcast News Understanding and Transcription Workshop.
6. Anacleto, J. C; Carvalho, A. F. P.; Pereira, E. N.; Ferreira, A. M; Carlos, A. F. (2008). Machines with good sense: How can computers become capable of sensible reasoning? Artificial Intelligence in Theory and Practice II, Vol. 276, pp. 195-204.
7. Bick, E. (2000). The Parsing System "Palavras": Automatic Grammatical Analysis of Portuguese in a Constraint Grammar Framework. PhD thesis. Aarhus University. Denmark University Press.
8. Carletta, J. (1996). Assessing Agreement on Classification Tasks: The Kappa Statistic. Computational Linguistics, Vol. 22, N. 2, pp. 249-254.
9. Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, Vol. 16, pp. 321-357.
10. Jorge, M. L. C. (2010). Sumarização automática multidocumento: seleção de conteúdo com base no modelo CST (Cross-document Structure Theory). Tese de Doutorado. Institute de Ciências Matemáticas e de Computação, Universidade de São Paulo.
11. Mann, W. C. and Thompson, S. A. (1987). Rhetorical Structure Theory: A Theory of Text Organization. Technical Report ISI/RS-87-190.
12. Miyabe, Y.; Takamura, H.; Okumura, M. (2008). Identifying Cross-Document Relations between Sentences. In the Proceedings of the Third International Joint Conference on Natural Language Processing, pp. 141-148.
13. Pardo, T. A. S. (2006). SENTER: Um Segmentador Sentencial Automático para o Português do Brasil. Série de Relatórios do NILC. NILC-TR-06-01. São Carlos-SP, Janeiro, 6p.
14. Prati, R. C; Batista, G. E. A. P. A.; Monard, M. C. (2008). Curvas ROC para avaliação de classificadores. IEEE América Latina, Vol. 6, N. 2.
15. Radev, D. R. (2000). A common theory of information fusion from multiple text sources, step one: Cross-document structure. In the Proceedings of the 1st ACL SIGDIAL Workshop on Discourse and Dialogue.
16. Radev, D. R. and McKeown, K. (1998). Generating natural language summaries from multiple on-line sources. Computational Linguistics, Vol. 24, N. 3, pp. 469-500.
17. Radev, D.R.; Otterbacher, J.; Zhang, Z. (2004). CST Bank: A Corpus for the Study of Cross-document Structural Relationships. In the Proceedings of Fourth International Conference on Language Resources and Evaluation.
18. Trigg, R. (1983). A Network-Based Approach to Text Handling for the Online Scientific Community. Ph.D. Thesis. Department of Computer Science, University of Maryland.
19. Trigg, R. and Weiser, M. (1987). TEXTNET: A network-based approach to text handling. ACM Transactions on Office Information Systems, Vol. 4, N. 1, pp. 1-23.
20. Witten, I. H. and Frank, E. (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufrnann.
21. Zhang, Z.; Otterbacher, J.; Radev, D. R. (2003). Learning Cross-document Structural Relationships using Boosting. In the Proceedings of the twelfth international conference on Information and knowledge management, pp. 124-130.
22. Zhang, Z. and Radev, D. R. (2004). Combining Labeled and Unlabeled Data for Learning Cross-Document Structural Relationships. In the Proceedings of IJCNLP, pp. 32-41.
23. Zhang, Z.; Blair-Goldensohn, S.; Radev, D. R. (2002). Towards CST-enhanced summarization. In the Proceedings of the Eighteenth National Conference on Artificial Intelligence, pp. 439-445.