View
4
Download
0
Category
Preview:
Citation preview
Prof. Pável Calado
•
•
•
•
•
•
•
•
•
3.2.1.1.1 Componente GATE Extractor
3.2.1.1.2 Componente FreeCite Extractor
3.2.1.2.1 Serviço Harvest Dblp
3.2.1.2.2 Serviço Harvest MS Academic
3.2.1.2.3 Serviço Harvest Google Scholar
•
•
•
•
•
3.2.3.7.1 Avaliação ao nível dos Serviços
•
•
•
3.2.3.7.2 Avaliação ao nível Global
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
[1] Feldman, Ronen; Hirsh, Haym (1997). Exploiting background information in knowledge discovery from text. Journal of Intelligent Information Systems, Boston, v.9, n.1, p.83-97.
[2] Appelt, Douglas E. and David J. Israel (1999). Introduction to information extraction technology. Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-99
[3] E. Riloff and J. Lorenzen, (1999). Extraction-Based Text Categorization: Generating Domain-specific Role Relationships Automatically. In: TomekStrzalkowski (editor). Natural Language Information Retrieval. Boston: Kluwer, and at http://www.cs.utah.edu/~riloff/psfiles/nlp-ir-chapter.ps
[4] M.-F. Moens (2006). Information Extraction: Algorithms and Prospects in a Retrieval Context. Springer-Verlag,
[5] Isaac Councill and C. Lee Giles and Min-Yen Kan. (2008) ParsCit: An open-source CRF reference string parsing package. In Proceedings of LREC. 28–30.
[6] Bick, Eckhard. (2000). The Parsing System "Palavras": Automatic Grammatical Analysis of Portuguese in a Constraint Grammar Framework. Aarhus: Aarhus University Press.
[7] Gaizauskas, R. and Wilks, Y. (1998). Information Extraction: Beyond Document Retrieval Computational Linguistics and Chinese Language Processing, Vol. 3, No. 2, 1998, pp. 17-59.
[8] Harris, Z. (1952). Discourse Analysis: A Sample Text. Language, 28(4), pp. 474-494. [9] Chomsky, N. (1965). Aspects of the Theory of Syntax. Cambridge: MIT Press. [10] M. Kraemer, H. Kaprykowsky, D. Keysers, and T. Breuel (2007). Bibliographic meta-data extraction
using probabilistic finite state transducers. In: Proceedings of ICDAR 2007, in press. [11] Brill, Eric (1992). A simple rule-based part of speech tagger. In Proceedings, Third Conference on
Applied Natural Language Processing, ACL, Trento, Italy. [12] Leslie Lamport (1986). LATEX: a document Preparation System. 2ª edição. Addison-Wesley
Publishing Company. [13] Funk, A.; Maynard, D.; Saggion, H.; Bontcheva, K. (2007). Ontological Integration of Information
Extracted from Multiple Sources. Multi-source Multilingual Information Extraction and Summarization (MMIES) workshop at Recent Advances in Natural Language Processing (RANLP07).
[14] Fleischman, Michael. (2001). Automated Subcategorization of Named Entities. In Proc. Conference of the European Chapter of Association for Computational Linguistic.
[15] Lee, Seungwoo; Geunbae Lee, G. (2005). Heuristic Methods for Reducing Errors of Geographic Named Entities Learned by Bootstrapping. In Proc. International Joint Conference on Natural Language Processing.
[16] Fleischman, Michael; Hovy. E. (2002). Fine Grained Classification of Named Entities. In Proc. Conference on Computational Linguistics.
[17] Bodenreider, Olivier; Zweigenbaum, P. (2000). Identifying Proper Names in Parallel Medical Terminologies. Stud Health Technol Inform 77.443-447, Amsterdam: IOS Press.
[18] Ferro, Lisa; Gerber, L.; Mani, I.; Sundheim, B.; Wilson G. (2005). TIDES 2005 Standard for the Annotation of Temporal Expressions. The MITRE Corporation.
[19] Witten, Ian. H.; Bray, Z.; Mahoui, M.; Teahan W. J. (1999). Using Language Models for Generic Entity Extraction. In Proc. International Conference on Machine Learning. Text Mining.
[20] Maynard, Diana; Tablan, V.; Ursu, C.; Cunningham, H.; Wilks, Y. (2001). Named Entity Recognition from Diverse Text Types. In Proc. Recent Advances in Natural Language Processing.
[21] Zhu, Jianhan; Uren, V.; Motta, E. (2005). ESpotter: Adaptive Named Entity Recognition for Web Browsing. In Proc. Conference Professional Knowledge Management. Intelligent IT Tools for Knowledge Management Systems.
[22] Brin, Sergey. (1998). Extracting Patterns and Relations from the World Wide Web. In Proc. Conference of Extending Database Technology. Workshop on the Web and Databases.
[23] Cohen, William W.; Sarawagi, S. (2004). Exploiting Dictionaries in Named Entity Extraction: Combining Semi-Markov Extraction Processes and Data Integration Methods. In Proc. Conference on Knowledge Discovery in Data.
[24] Bick, Eckhard (2004). A Named Entity Recognizer for Danish. In Proc. Conference on Language Resources and Evaluation.
[25] R. Bunescu, R. Ge, R. J. Mooney, E. Marcotte, and A. K. Ramani (2002). Extracting gene and protein names from biomedical abstracts. Unpublished Technical Note, Available from http://www.cs.utexas.edu/users/ml/publication/ie.html.
[26] K. Humphreys, G. Demetriou, and R. Gaizauskas (2000). Two applications of information extraction to biological science journal articles: Enzyme interactions and protein structures. In Proceedings of 2000 the Pacific Symposium on Biocomputing (PSB-2000), pp. 502–513.
[27] Tsuruoka, Yoshimasa; Tsujii, J. (2003). Boosting Precision and Recall of Dictionary-Based Protein Name Recognition. In Proc. Conference of Association for Computational Linguistics. Natural Language Processing in Biomedicine.
[28] Simpkins N., M. Groenendijk. (1994). The ALEP Project. Cray Systems / CEC, Luxemburg. [29] Evans, Richard. (2003). A Framework for Named Entity Recognition in the Open Domain. In Proc.
Recent Advances in Natural Language Processing. [30] TIPSTER Architecture Committee. (1994). TIPSTER Text Phase II Architecture Concept. TIPSTER
working paper 1994, available at http://www.cs.nyu.edu/tipster. [31] S. Soderland (1999). Learning information extraction rules for semi-structured and free text, Machine
Learning. vol. 34. [32] T. Jayram, R. Krishnamurthy, S. Raghavan, S. Vaithyanathan and H. Zhu (2006). Avatar information
extraction system. In IEEE Data Engineering Bulletin. [33] D. E. Appelt and B. Onyshkevych (1998). The common pattern specification language. In TIPSTER
workshop. [34] H. Cunningham, D. Maynard, K. Bontcheva, and V. Tablan (2002). Gate: A framework and graphical
development environment for robust NLP tools and applications. In Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics.
[35] D. E. Appelt, J. R. Hobbs, J. Bear, D. J. Israel, and M. Tyson (1993). Fastus: A finite-state processor for information extraction from real-world text. In IJCAI, pp. 1172–1178.
[36] Lin D. (1995). University of Manitoba: Description of the PIE System as Used for MUC-6. In Proceedings of the Sixth Conference on Message Understanding (MUC-6), Columbia, Maryland.
[37] Nuno Cardoso (2008). REMBRANDT - Reconhecimento de Entidades Mencionadas Baseado em Relações e Análise Detalhada do Texto. Em Cristina Mota& Diana Santos (eds.), Desafios na avaliação conjunta do reconhecimento de entidades mencionadas: O Segundo HAREM. Linguateca.
[38] E. Riloff (1993). Automatically constructing a dictionary for information extraction tasks. In AAAI, pp. 811–816.
[39] Kim, J., & Moldovan, D. (1993). Acquisition of semantic patterns for information extraction from corpora. Proceedings of the Ninth IEEE Conference on Artificial Intelligence for Applications. IEEE Computer Society Press. pp. 171–176.
[40] Soderland, S., Fisher, D., Aseltine, J., &Lehnert, W. (1995). CRYSTAL: Inducing a conceptual dictionary. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. pp. 1314–1321.
[41] Huffman, S. (1996). Learning information extraction patterns from examples. In S. Wermter, E. Riloff, & G. Scheller (Eds.), Connectionist, statistical, and symbolic approaches to learning for natural language processing. Berlin: Springer.
[42] Aseltine J., (1999). WAVE: An Incremental Algorithm for Information Extraction. In Proceedings of the AAAI 1999 Workshop on Machine Learning for Information Extraction.
[43] Califf, M.E. & Mooney, R. (1997). Relational learning of pattern-match rules for information extraction. Working Papers of ACL-97 Workshop on Natural Language Learning, pp. 9–15.
[44] Thompson H. (1995). MULTEXT Workpackage 2 Milestone B Deliverable Overview. LRE 62-050 MULTEXT
[45] K. Seymore, A. McCallum, and R. Rosenfeld (1999). Learning Hidden Markov Model structure for information extraction. In Papers from the AAAI99 Workshop on Machine Learning for Information Extraction. pp. 37–42.
[46] V. R. Borkar, K. Deshmukh, and S. Sarawagi (2001). Automatic text segmentation for extracting structured records. In Proc. ACM SIGMOD International Conf. on Management of Data, Santa Barabara, USA.
[47] D. M. Bikel, S. Miller, R. Schwartz, and R. Weischedel (1997). Nymble: A high-performance learning name-finder. In Proceedings of ANLP-97, pp. 194–201.
[48] Chieu, H. L. and NG, H. T. (2002). A maximum entropy approach to information extraction from semi structured and free text. In Proceedings of the 18th National Conference on Artificial Intelligence (AAAI).
[49] Mccallum, A., Freitag, D., and Pereira, F. (2000). Maximum entropy Markov models for information extraction and segmentation. In Proceedings of the 17th International Conference on Machine Learning (ICML).
[50] McCallum, Andrew Kachites (2002). MALLET: A Machine Learning for Language Toolkit. [51] Cohen, William W. Minorthird (2004). Methods for Identifying Names and Ontological Relations in
Text using Heuristics for Inducing Regularities from Data. [52] B. Settles. ABNER (2005). An open source tool for automatically tagging genes, proteins, and other
entity names in text. Bioinformatics, 21(14). pp. 3191-3192. [53] F. Peng and A. McCallum (2004). Accurate information extraction from research papers using
conditional random fields,” in HLT-NAACL, pp. 329–336. [54] Carpenter, Bob. (2007). LingPipe for 99.99% Recall of Gene Mentions. Proceedings of the 2nd
BioCreative workshop. Valencia, Spain. [55] Nadeau, D. (2007). Semi-Supervised Named Entity Recognition: Learning to Recognize 100 Entity
Types with Little Supervision, PhD thesis, University of Ottawa. [56] Hamish Cunningham, Diana Maynard, C. Ursu K. Bontcheva, V. Tablan, and M. Dimitrov. (2002).
Developing language processing components with GATE. Technical report, University of Sheffield, Sheffield, UK.
[57] Leaman B, Gonzalez G. BANNER (2008). An executable survey of advances in biomedical named entity recognition. Pac. Symp. Biocomput. 13: 652–663.
[58] Jin Y, McDonald R, Lerman K, et al (2006). Automated recognition of malignancy mentions in biomedical literature. BMC Bioinform. 7:492.
[59] Kim, Youngho, Jung, Yuchul and Myaeng, S.-H. (2007). Identifying Opinion Holders in Opinion Text from Online Newspapers. Proceedings of the 2007 IEEE International Conference on Granular Computing.
[60] Aitao Chen, FuchunPeng, Roy Shan, and Gordon Sun (2006). Chinese named entity recognition with conditional probabilistic models. In 5th SIGHAN Workshop on Chinese Language Processing, Australia.
[61] Ferrucci, D. and Lally, A. (2004) UIMA: An Architectural Approach to Unstructured Information Processing in the Corporate Research Environment. Natural Language Engineering.
[62] S. Bird, D. Day, J. Garofolo, J. Henderson, C. Laprun, and M. Liberman. (2000). ATLAS: A flexible and extensible architecture for linguistic annotation. In Proceedings of the Second International Conference on Language Resources and Evaluation, Athens. Barcelona.
[63] I. H. Witten, K. J. Don, M. Dewsnip, and V. Tablan (2004). Text mining in a digital library. Int. Journal on Digital Libraries. 4(1): 56–59.
[64] H. Saggion, H. Cunningham, K. Bontcheva, D. Maynard, O. Hamza, and Y. Wilks (2003). Multimedia Indexing through Multisource and Multilingual Information Extraction; the MUMIS project. Data and Knowledge Engineering.
[65] Cunningham H., M. Freeman, W.J. Black. (1994). Software Reuse, Object-Orientated Frameworks and Natural Language Processing. Conference on New Methods in Naing, Manchester.
Recommended