Learning to extract and summarize hot item features from multiple auction web sites

Knowledge and Information Systems

Volumn 14, Issue 2, 2008, Pages 143-160

  Wong, Tak Lam ^a   Lam, Wai ^b  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Conditional random fields; Information extraction; Web mining

Indexed keywords

DATA MINING; INFORMATION RETRIEVAL; RANDOM PROCESSES;

AMOUNT OF INFORMATION; AUCTION SITES; CONDITIONAL RANDOM FIELD; GRAPHICAL MODEL; INTER-DEPENDENCES; PRODUCT FEATURE; UNIFIED FRAMEWORK; WEB MINING;

WEBSITES;

EID: 38649112310     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-007-0078-2     Document Type: Conference Paper

References (32)

1. Agichtein E, Ganti V (2004) Mining reference tables for automatic text segmentation. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD), pp 20-29
2. Auction Sotware Review (2003) In http://www.auctionsoftwarereview.com/ article-ebay-statistics.asp
3. Aumann Y, Feldman R, Liberzon Y, Rosenfeld B, Schler J (2006). Visual information extraction. Knowl Inform Syst 10(1):1-15
4. Bunescu R, Mooney R (2004) Collective information extraction with relational markov networkds. In: Proceedings of the 42nd annual meeting of the association for computational linguistics (ACL), pp 439-446
5. Chang C, Lui SC (2001) IEPAD: Information extraction based on pattern discovery. In: Proceedings of the tenth international conference on world wide web (WWW), pp 681-688
6. 2 an adaptive algorithm for information extraction from web-related texts. In: Proceedings of the seventeenth international joint conference on artificial intelligence (IJCAI), pp 1251-1256
7. Collins M (2002) Ranking algorithms for named-entity extraction: boosting and the voted perceptron. In: Proceedings of the annual meeting of the association for computational linguistics (ACL), pp 489-496
8. Crescenzi V, Mecca G (2004) Automatic information extraction from large websites. J ACM 51(5):731-779
9. Crescenzi V, Mecca G, Merialdo P (2001) ROADRUNNER: Towards automatic data extraction from large web sites. In: Proceedings of the 27th very large databases conference (VLDB), pp 109-118
10. Etzioni O, Cafarella M, Kok S, Popescu A, Shaked T, Soderland S, Weld D, Yates A (2005) Unsupservised named-entity extraction from the web: An experimental study. Artif Intell 165(1): 91-134
11. Feldman R, Rosenfeld B, Fresko M (2006) TEG - a hybrid approach to information extraction. Knowl Inform Syst 9(1):1-18
12. Freitag D, McCallum A (2000) Information extraction with HMM structures learned by stochastic optimization. In: Proceedings of the seventeenth national conference on artificial intelligence (AAAI), pp 584-589
13. Ghani R (2005) Price prediction and insurance for online auctions. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD), pp 411-418
14. Ghani R, Simmons H (2004) Predicting the end-price of online auctions. In: International workshop on data mining and adaptive modelling methods for economics and management
15. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD), pp 168-177
16. Kschischang F, Frey B, Loeliger H (2001) Factor graphs and the sum-product algorithm. IEEE Trans on Inform Theory 47(2):498-519
17. Kushmerick N (2000) Wrapper induction: Efficiency and expressiveness. Artif Intell 118(1-2): 15-68
18. Kushmerick N, Thomas B (2002) Adaptive information extraction: Core technologies for information agents. In: Intelligents information agents R&d in europe: An agentLink perspective, pp 79-103
19. Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of eighteenth international conference on machine learning (ICML), pp 282-289
20. Li Z, Ng WK, Sun A (2005) Web data extraction based on structural similarity. Knowl Inform Syst 8(4):438-491
21. Liu B, Grossman R, Zhai Y (2003) Mining data records in web pages. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD), pp 601-606
22. Mani I, Maybury M (1999) In advances in automatic text summarization. MIT press, Cambridge
23. McCallum A, Jensen D (2003) A note on the unification of information extraction and data mining using conditional-probability, relational models. In: Proceedings of the IJCAI workshop on learning statistical models from relational data
24. McCallum A, Wellner B (2003) Toward conditional models of identity uncertainty with application to proper noun coreference. In: Proceedings of the IJCAI workshop on information integration on the web
25. Muslea I, Minton S, and Knoblock C (2001) Hierarchical wrapper induction for semistructured information sources. J Auton Agents Multi-Agent Syst 4(1-2):93-114
26. Popescu A, Etzioni O (2005) Extracting product features and opinions from reviews. In: Proceedings of the human language technology conference conference on empirical methods in natural language processing, pp 339-346
27. Wang J, Karypis G (2005) On efficiently summarizing categorical databases. Knowl Inform Syst 9(1):19-37
28. Wellner B, McCallum A, Peng F, Hay M (2004) An integrated, conditional model of information extraction and coreference with application to citation matching. In: Proceedings of the 20th conference on uncertainty in artificial intelligence (UAI), pp 593-601
29. Wong TL, Lam W (2004) A probabilistic approach for adapting information extraction wrappers and discovering new attributes. In: Proceedings of the 2004 IEEE international conference on data mining (ICDM), pp 257-264
30. Wong TL, Lam W, Chan SK (2006) Extracting and summarizing hot items features across different auction web sites. In: The tenth Pacific-Asia conference on knowledge discovery and data mining (PAKDD), pp 334-345
31. Wong TL, Lam W (2007) Adapting web information extraction knowledge via mining site- invariant and site-dependent features. ACM Trans Internet Technol (in press)
32. Yi J, Niblack W (2005) Sentiment mining in web fountain. In: Proceedings of the 21st international conference on data engineering (ICDE), pp 1073-1083