Mining text data springer

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Text data are copiously found in many domains, such as the Web, social networks, newswire services, and libraries. With the increasing ease in archival of human. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the Springer Shop; Amazon.com;5/5(1). Text Mining also referred to as Data Mining on Text, has emerged at the intersection of several research areas, some focused on data analytics and others focused more.
MINING TEXT DATA Edited by CHARUC.AGGARWAL IBM T. J. Watson Research Center, Yorktown Heights, NY, USA CHENGXIANGZHAI University of Illinois at . Springer grants text- and data-mining rights to subscribed content, provided the purpose is non-commercial research. Demand for TDM has been low up to now, but is. Text data are copiously found in many domains, such as the Web, social networks, newswire services, and libraries. With the increasing ease in archival of human. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the Springer Shop; Amazon.com;5/5(1). Text Mining also referred to as Data Mining on Text, has emerged at the intersection of several research areas, some focused on data analytics and others focused more.
  • 1.

    Adafre, S.F. and de Rijke, M. (2006). Finding similar sentences acorss multiple languages in Wikipedia. 11 th Conference of the European Chapter of the Association for Computational Linguistics, pp. 6269.Google Scholar

  • 2.

    Ballesteros, L. and Croft, W. (1997). Phrasal translation and query expansion techniques for cross-language information retrieval. In Proceedings of SIGIR Conf. pp. 84-91.Google Scholar

  • 3.

    Berger, A. and Lafferty, J. (1999). Information retrieval as statistical translation. In Proceedings of SIGIR Conf., pp. 222-229.Google Scholar

  • 4.

    Braschler, M., and Schäuble, P. (1998). Multilingual information retrieval based on document alignment techniques. ECDL 98: Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries, mining text data springer, pp. 183197.Google Scholar

  • 5.

    Braschler, M., and Schäuble, P. (2001). Experiments with the Eurospider Retrieval System for CLEF 2000, in Proceedings of CLEF Conference. pp. 140-148.Google Scholar

  • 6.

    Brown, P., Della Pietra, S., Della Pietra, V., and Mercer, R. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2), pp. 263-311.Google Scholar

  • 7.

    Cao, G., Gao, J., Nie, J.Y. (2007) A system mining and metals group mine large-scale bilingual dictionaries from monolingual Web pages, MT Summit, pp. 57-64.Google Scholar

  • 8.

    Carbonell, J.G, Yang, Y, Frederking, R.E., Brown, R., Geng, Y. and Lee, D. (1997) Translingual information retrieval: A comparative evaluation. In: Proceedings of the International Joint Conference on Arti?cial Intelligence (IJCAI 97).Google Scholar

  • 9.

    Chiang, D., (2005) A Hierarchical Phrase-Based Model for Statistical Machine Translation. ACL.Google Scholar

  • 10.

    Chen, J., Nie, J.Y., (2000) Automatic construction of parallel English-Chinese corpus for cross-language information retrieval. ANLP pp. 21-28Google Scholar

  • 11.

    Chen, H.H., Lin, W.C. and Yang, C.H. (2006). Translation-Transliterating Named Entities for Multilingual Information Access. Journal of the American Society for Information Science and Technology, 57(5):645-659CrossRefGoogle Scholar

  • 12.

    Cheng, P., Teng, J., Chen, R., Wang, J., Lu, W., and Chien, L. (2004). Translating Unknown Queries with Web Corpora for Cross- Language Information Retrieval. In Proceedings of SIGIR Conf., pp.162-169.Google Scholar

  • 13.

    Dumais, S. T., Letsche, T. A., Littman, M. L. and Landauer, T. K. (1997) Automatic cross-language retrieval using Latent Semantic Indexing. AAAI Spring Symposuim on Cross-Language Text and Speech Retrieval, March 1997.Google Scholar

  • 14.

    Franz, M., McCarley, J.S. and Koukos, S. (1999) Ad hoc and multilingual information retrieval at IBM. Proceedings of the Seventh Text Retrieval Conference (TREC-7), pp. 157168.Google Scholar

  • 15.

    Fung, P. (1995). A Pattern Matching Method for Finding Noun and Proper Noun Translations from Noisy Parallel Corpora. Proceedings of the Association for Computational Linguistics, pp. 236-243.Google Scholar

  • 16.

    Pascale Fung and Yuen Yee Lo. 1998. An IR approach for translating new words from nonparallel, comparable texts. Proceedings of COLING-ACL98, pp. 414 420.Google Scholar

  • 17.

    Fung, P. and McKeown, K. (1997) Finding terminology translations from non-parallel corpora. In: The 5th Annual Workshop gtx 1060 3g ocv1 mining Very Large Corpora.Google Scholar

  • 18.

    Fung, P. and Cheung, P. (2004) Multilevel boot-strapping for extracting parallel sentences from a quasi parallel corpus. Conference on Empirical Methods in Natural Language Processing (EMNLP 04), pp. 10511057.Google Scholar

  • 19.

    Gale, W. A., Church K. W. 1993. A Program for Aligning Sentences in Bilingual Corpora. Computational Linguistics, 19(3): 75-102.Google Scholar

  • 20.

    Galley, M., Hopkins, M., Knight, K., Marcu, D., (2004) Whats in a translation rule? HLT-NAACL, pp. 273-280Google Scholar

  • 21.

    Pablo Gamallo Otero, Isaac Gonzalez Lopez, (2009) Wikipedia as Multilingual Source of Comparable Corpora, Proceedings of the 3rd Workshop on Building and Using Comparable Corpora, LREC 2010, pp. 2125Google Scholar

  • 22.

    Gao, J., Nie, J.Y., Xun, E., Zhang, J., Zhou, M., and Huang, C. (2001). Improving query translation for cross-language information retrieval using statistical models. In Proceedings of SIGIR Conf., pp. 96-104.Google Scholar

  • 23.

    Gao, J., Zhou, M., Nie, J.Y., He, H., Chen, W. (2002) Resolving query translation ambiguity using a decaying co-occurrence model and syntactic dependence relations. SIGIR, pp. 183-190Google Scholar

  • 24.

    Gao, J., Nie, J.Y. (2006) Study of Statistical Models for Query Translation: Finding a Good Unit of Translation. SIGIR, pp 194- 201, 2006.Google Scholar

  • 25.

    Gao, J., He, X., Nie. J.Y. (2010) Clickthrough-based translation models for web search: from word models to phrase models. CIKM, pp 1139-1148, mining text data springer Scholar

  • 26.

    Hong, Gumwon, Li, Chi-Ho, Zhou, Ming and Rim, Hae-Chang (2010) An Empirical Study on Web Mining of Parallel Data, COLING, pp. 474482.Google Scholar

  • 27.

    Huang, Degen, Zhao, Lian, Li, Lishuang Yu, Haitao (2010) Mining Large-scale Comparable Corpora from Chinese-English News Collections, COLING, pp. 472-480.Google Scholar

  • 28.

    Huang, F., Zhang, Y., and Vogel, S. (2005). Mining Key Phrase Translations from Web Corpora. In Proceedings of HLT-EMNLP Conf., pp. 483-490.Google Scholar

  • 29.

    Jeon, J. Lavrenko, V. and Manmatha, R. (2003) Automatic Image Annotation and Retrieval using Cross-Media Relevance Models, SIGIR, pp. 119-126.Google Scholar

  • 30.

    Jeong, K.S., Myaeng, S.H., Lee, J.S, and Choi, K.S., (1999) Automatic identification and back-transliteration of foreign words for information retrieval, Information Processing and Management, 35(4), pp. 523-540.CrossRefGoogle Scholar

  • 31.

    Ji, Heng (2009) Mining Name Translations from Comparable Corpora by Creating Bilingual Information Networks, Proceedings of the 2 nd Workshop on Building and Using Comparable Corpora, ACL-IJCNLP 2009, pages3437.Google Scholar

  • 32.

    Koehn, mining text data springer, P., Och, F.J., Marcus, D., (2003) Statistical phrase-based translation, In Proceedings of HLT-NAACL, pp. 48-54.Google Scholar

  • 33.

    Koehn, P. (2009) Statistical Machine Translation. Cambridge University Press.Google Scholar

  • 34.

    Kraaij, W., Nie, J.Y., and Simard, M. (2003). Embedding Web-Based Statistical Translation Models in Cross-Language Information Retrieval. Computational Linguistics, 29(3): 381-420.Google Scholar

  • 35.

    Kumano, T. and Tanaka, H., Tokunaga, T. (2007) Extracting phrasal alignments from comparable corpora by using joint probability SMT model. 11th International Online mining litecoin on Theoretical and Methodological Issues in Machine Translation (TMI07).Google Scholar

  • 36.

    Kuo, J.S., Li, H., and Yang Y.K (2006). Learning Transliteration Lexicon from the Web. In the Proceedings of COLING/ACL, pp.1129-1136Google Scholar

  • 37.

    Lam, W., Chan, S.K., and Huang, R. (2007). Named Entity Translation Matching and Learning: With Application for Mining Unseen Translations. ACM Transactions on Information Systems, 25(1), pp.Google Scholar

  • 38.

    Liu, Y., Jin R. and Chai, Joyce Y. (2005). A maximum coherence model for dictionary-based cross-language information retrieval, In Proceedings of SIGIR conf., pp. 536-543.Google Scholar

  • 39.

    Lu, W. Chien, L.F. and Lee, H. (2004). Anchor Text Mining for Translation of Web Queries: A Transitive Translation Approach. ACM Transactions on Information Systems, Vol.22, pp. 242-269.CrossRefGoogle Scholar

  • 40.

    Ma, X. and Liberman, M., (1999). Bits: A Method for Bilingual Text Search over the Web. Proceedings of Machine Translation Summit VII.Google Scholar

  • 41.

    Munteanu, D. S., Marcu, Emc2 mining. (2005) Improving Machine Translation Performance by Exploiting Non-Parallel Corpora. 2005. Computational Linguistics. 31(4). pp: 477-504.Google Scholar

  • 42.

    Munteanu, D. S. and Marcu D. (2006). Extracting parallel subsentential fragments from non-parallel corpora. ACL, pp. 8188.Google Scholar

  • 43.

    Nagata, M., Saito, T., and Suzuki, K. (2001). Using the web as a bilingual dictionary. In Proceedings of the Workshop on Data-Driven Methods in Machine Translation mining text data springer ACL Conf.), pp. 1-8.Google Scholar

  • 44.

    Nie, J.Y., Cai, J. (2001) Filtering parallel corpora of norilsk nickel mining company pages, IEEE symposium on NLP and Knowledge Engineering, pp. 453-458.Google Scholar

  • 45.

    Nie, J.Y., Simard, M., Isabelle, P., Durand, R. (1999) Cross-Language Information Retrieval based on Parallel Texts and Automatic Mining of Parallel Texts in the Professions mining, Mining text data springer Proceedings of SIGIR Conf., pp. 74-81Google Scholar

  • 46.

    Och, F., and Ney, H. (2002) Discriminative Training and Maximum Entropy Models for Statistical Machine Translation. ACL, pp. 295-302Google Scholar

  • 47.

    Och, F. (2003). Minimum error rate training in statistical machine translation. In Proceedings of ACL. pp. 160-67Google Scholar

  • 48.

    Oumohmed, A.I., Mignotte, M., Nie, J.Y. (2005) Semantic-Based Cross-Media Image Retrieval, Pattern Recognition and Image Analysis: Third International Conference on Advances in Pattern Recognition (ICAPR), LNCS 3687, pp. 414-423.Google Scholar

  • 49.

    Potthast, M., Stein, B., Anderka, M. (2008) A Wikipedia-based Multilingual Retrieval Model. ECIR, LNCS 4956, pp. 522530.Google Scholar

  • 50.

    Qu, Y., Grefenstette, G., and Evans, D. A. (2003). Automatic transliteration for Japanese-to-English text retrieval. In Proceedings of SIGIR Conference, mining text data springer, pp. 353-360.Google Scholar

  • 51.

    Rapp, R. (1995). Identifying Word Translations in Non-Parallel Texts. Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pp. 320-322.Google Scholar

  • 52.

    Resnik, P., (1999) Mining the Web for Bilingual Text, 37th Annual Meeting of the Association for Computational Linguistics (ACL99).Google Scholar

  • 53.

    Resnik P. and Smith. N.A. (2003) The Web as a Parallel Corpus, Computational Linguistics, 29(3), pp. 349-380, September 2003.Google Scholar

  • 54.

    Sheridan, P. and Ballerini, J. P. (1996). Experiments in multilingual information retrieval using the SPIDER system. In Proceedings of SIGIR Conf., pp. 58-65.Google Scholar

  • 55.

    Schönhofen, P., Benczúr, A., Bíró, I., Csalogány, K. (2007) Performing cross-language retrieval with Wikipedia, CLEF-2007 (http://www.clefcampaign.org/2007/working notes/schonhofenCLEF2007.pdf)Google Scholar

  • 56.

    Shi, L., Niu, C., Zhou, M., and Gao, J. (2006) A DOM Tree Alignment Model for Mining Parallel Data from the Web, ACL, pp. 489-496.Google Scholar

  • 57.

    Smith, J. R., Quirk, C., and Toutanova, K. (2010) Extracting parallel sentences from comparable corpora using document level alignment. HLT, pp. 403411Google Scholar

  • 58.

    Sproat, R., Tao, T., Zhai, C. (2006) Named Entity Transliteration with Comparable Corpora. In Proceedings of ACL.Google Scholar

  • 59.

    Tuomas Talvensaari, Jorma Laurikkala, Kalervo Järvelin, Martti Juhola (2006) A study on automatic creation of a comparable document collection in cross-language information retrieval, Journal of Documentation, Vol. 62 No. 3, pp. 372-387Google Scholar

  • 60.

    Tuomas Talvensaari, Jorma Laurikkala, Kalervo Järvelin, Martti Juhola, and Heikki Keskustalo (2007). Creating and exploiting a rating gpu for mining corpus in cross-language information retrieval. ACM Trans. Inf. Syst. 25, 1, Article 4.Google Scholar

  • 61.

    Utiyama M. and Isahara, H. (2003) Reliable Measures for Aligning Japanese-English News Articles and Sentences. ACL, pp. 7279.Google Scholar

  • 62.

    Jinxi Xu, W. Bruce Croft (1996) Query Expansion Using Local and Global Document Analysis. SIGIR, pp. 4-11Google Scholar

  • 63.

    Yang, Christopher C., and Kar Wing Li. 2003. Automatic construction of English/Chinese parallel corpora. Journal of the American Society for Information Science and Technology, 54(8), pp. 730742.CrossRefGoogle Scholar

  • 64.

    Zhang, Y. and Vines, P. (2004). Using the Web for Automated Translation Extraction in Cross-Language Information Retrieval. In Proceedings of SIGIR Conf., pp.162-169.Google Scholar

  • 65.

    Zhang, Y., Huang, F., Vogel, S. (2005) Mining Translations of OOV Mining text data springer from the Web through Cross-lingual Query Expansion, SIGIR, pp. 669-670.Google Scholar

  • 66.

    Zhao, B., and Vogel, S. (2002), mining text data springer. Adaptive Parallel Sentences Mining from Web Bilingual News Collection. In Proceedings of IEEE international conference on data mining, pages 745-750.Google Scholar

  • Источник:




    Mining Text Data: Special Features and Patterns | Springer for Research & Development

    Text Mining also referred to as Data Mining on Text, has emerged at the intersection of several research areas, some focused on data analytics and others focused more. Text mining is an increasingly important research field because of the necessity of obtaining knowledge from the enormous number of text documents available. Covers Text Embedded with Heterogeneous and Multimedia Data All chapters contain a comprehensive survey including the key research content on the topic. MINING TEXT DATA Edited by CHARUC.AGGARWAL IBM T. J. Watson Research Center, Yorktown Heights, NY, USA CHENGXIANGZHAI University of Illinois at . Springer grants text- and data-mining rights to subscribed content, provided the purpose is non-commercial research. Demand for TDM has been low up to now, but is. Text data are copiously found in many domains, such as the Web, social networks, newswire services, and libraries. With the increasing ease in archival of human.

    MINING TEXT DATA Edited by CHARUC.AGGARWAL IBM T. J. Watson Research Center, Yorktown Heights, NY, USA CHENGXIANGZHAI University of Illinois at . Text Mining also referred to as Data Mining on Text, has emerged at the intersection of several research areas, some focused on data analytics and others focused more. Text mining is an increasingly important research field because of the necessity of obtaining knowledge from the enormous number of text documents available.


  • Abidi, S. S. R. (2001). “Knowledge Management in Healthcare: Towards ‘Knowledge-driven’ Decision-support Services,” International Journal of Medical Informatics, 63, 5–18.PubMedCrossRefGoogle Scholar

  • Acir, N. and Guzelis, C. (2004). “Automatic Spike Detection in EEG by a Two-stage Procedure Based on Support Vector Machines,” Computers in Biology and Medicine, 34(7), 561–575.PubMedCrossRefGoogle Scholar

  • Ackerman, M. J. (1991). “The Visible Human Project,” Journal of Biocommunication, 18(2), 14.PubMedGoogle Scholar

  • Ahmad, S., Gromiha, M. M., and Sarai, A. (2004). “Analysis and Prediction of DNA-binding Proteins and Their Binding Residues Based on Composition, Sequence, and Structural Information,” Bioinformatics, 20(4), 477–486.PubMedCrossRefGoogle Scholar

  • Altschul, S. F., Madden, T. L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D. J. (1997). “Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs,” Nucleic Acids Research, 25(17), 3389–3402.PubMedCrossRefGoogle Scholar

  • Antani, S., Lee, D. J., Long, L. R., and Thoma, G. R. (2004). “Evaluation of Shape Similarity Measurement Methods for Spine X-ray Images,” Journal of Visual Communication and Image Representation, 15, 285–302.CrossRefGoogle Scholar

  • Baclawski, K., Cigna, J., Kokar, M. W., Mager, P., and Indurkhya, B. (2000). “Knowledge Representation and Indexing Using the Unified Medical Language System,” in Proceedings of the Pacific Symposium on Biocomputing, 493–504.Google Scholar

  • Barrera, J., Cesar-Jr, R. M., Ferreira, J. E., and Gubitoso, M. D. (2004). “An Environment for Knowledge Discovery in Biology,” Computers in Biology and Medicine, 34, 427–447.PubMedCrossRefGoogle Scholar

  • Baujard, O., Baujard, V., Aurel, S., Boyer, C., and Appel, R. D. (1998). “Trends in Medical Information Retrieval on the Internet,” Computers in Biology and Medicine, 28, 589–601.PubMedCrossRefGoogle Scholar

  • Belacel, B., Cuperlovic-Culf, M., Laflamme, M., and Ouellette, R. (2004). “Fuzzy J-Means and VNS Methods for Clustering Genes from Microarray Data,” Bioinformatics, 20(11), 1690–1701.PubMedCrossRefGoogle Scholar

  • Belew, R. K. (1989). “Adaptive Information Retrieval: Using a Connectionist representation to Retrieve and Learn about Documents,” in Proceedings of the 12thACM-SIGIR Conference, Cambridge, MA, June 1989.Google Scholar

  • Berman, J. J. (2002). “Confidentiality Issues for Medical Data Miners,” Artificial Intelligence in Medicine, 26(1–2), 25–36.PubMedCrossRefGoogle Scholar

  • Blaschke, C., Andrade, M. A., Ouzounis, C. and Valencia, A. (1999). “Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions,” in Proceedings of the International Conference on Intelligent Systems for Molecular Biology, 60–67.Google Scholar

  • Bodenreider, O. and McCray, A. T. (2003). “Exploring Semantic Groups through Visual Approaches,” Journal of Biomedical Informatics, 36, 414–432.PubMedCrossRefGoogle Scholar

  • Breiman, L. and Spector, P. (1992). “Submodel Selection and Evaluation in Regression: The X-random Case,” International Statistical Review, 60(3), 291–319.CrossRefGoogle Scholar

  • Brown, M. P. S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares, M., and Haussler, D. (2000). “Knowledge-based Analysis of Microarray Gene Expression Data by Using Support Vector Machines,” in Proceedings of the National Academy of Sciences, 97, 262–267.CrossRefGoogle Scholar

  • Campbell, K. E., Oliver, D. E., and Shortliffe, E. H. (1998). “The Unified Medical Language System: Toward a Collaborative Approach for Solving Terminologic Problems,” Journal of the American Medical Informatics Association, 5(1), 12–16.PubMedGoogle Scholar

  • Carbonell, J. G. Michalski, R. S., Mitchell, T. M. (1983). “An Overview of Machine Learning,” in R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.), Machine Learning, An Artificial Intelligence Approach, Palo Alto, CA: Tioga.Google Scholar

  • Cavideds, J. E. and Cimino, J. J. (2004). “Towards the Development of a Conceptual Distance Metric for the UMLS,” Journal of Biomedical Informatics, 37, 77–85.CrossRefGoogle Scholar

  • Chapman, W. W., Dowling, J. N., and Wagner, M. M. (2004). “Fever Detection from Free-text Clinical Records for Biosurveillance,” Journal of Biomedical Informatics, 37, 120–127.PubMedCrossRefGoogle Scholar

  • Chau, M. and Chen, H. (2003). “Comparison of Three Vertical Search Spiders,” IEEE Computer, 36(5), 56–62.Google Scholar

  • Chau, M. and Chen, H. (2004). “Using Content-based and Link-based Analysis in Building Vertical Search Engines,” in Proceedings of the International Conference on Asian Digital Libraries, Shanghai, China, December 13–17, 2004.Google Scholar

  • Chau, M., Xu, J. J., and Chen, H. (2002). “Extracting Meaningful Entities from Police Narrative Reports,” in Proceedings of the National Conference for Digital Government Research, Los Angeles, California, USA, May 19–22, 2002, 271–275.Google Scholar

  • Chen, H. (2001). Knowledge Management Systems: A Text Mining Perspective, Tucson, AZ: The University of Arizona.Google Scholar

  • Chen, H. and Chau, M. (2004). “Web Mining: Machine Learning for Web Applications,” Annual Review of Information Science and Technology, 38, 289–329.CrossRefGoogle Scholar

  • Chen, H. and Kim, J. (1995). “GANNET: A Machine Learning Approach to Document Retrieval,” Journal of Management Information Systems, 11(3), 9–43.Google Scholar

  • Chen, H., Lally, A. M., Zhu, B., and Chau, M. (2003). “HelpfulMed: Intelligent Searching for Medical Information over the Internet,” Journal of the American Society for Information Science and Technology, 54(7), 683–694, 2003.CrossRefGoogle Scholar

  • Chen, H. and Ng, T. (1995). “An Algorithmic Approach to Concept Exploration in a Large Knowledge Network (Automatic Thesaurus Consultation): Symbolic Branch and Bound Search vs. Connectionist Hopfield Net Activation,” Journal of the American Society for Information Science, 46(5), pp. 348–369.CrossRefGoogle Scholar

  • Chinchor, N. A. (1998). “Overview of MUC-7/MET-2,” in Proceedings of the Seventh Message Understanding Conference (MUC-7), Virginia, USA, April 29–May 1, 1998.Google Scholar

  • Cimino, J. J., Min, H., and Perl, Y. (2003) “Consistency across the Hierarchies of the UMLS Semantic Network and Metathesaurus,” Journal of Biomedical Informatics, 36, 450–461.PubMedCrossRefGoogle Scholar

  • Cios, K. J. and Moore, G. W. (2002). “Uniqueness of Medical Data Mining,” Artificial Intelligence in Medicine, 26(1–2), 25–36.Google Scholar

  • Cohen, P. R. and Feigenbaum, E. A. (1982). The Handbook of Artificial Intelligence: Volume III, Reading, MA: Addison-Wesley.Google Scholar

  • Dawes, M. and Sampson, U. (2003). “Knowledge Management in Clinical Practice: A Systematic Review of Information Seeking Behavior in Physicians,” International Journal of Medical Informatics, 71, 9–15.PubMedCrossRefGoogle Scholar

  • Dickerson, J. A., Berleant, D., Cox, Z., Fulmer, A. W., and Wurtele, E. (2003). “Creating and Modeling Metabolic and Regulatory Networks Using Text Mining and Fuzzy Expert Systems,” in J. T. L. Wang, C. H. Wu, and P. P. Wang (Eds.), Computational Biology and Genome Informatics, World Scientific.Google Scholar

  • Dreiseitl, S., Ohno-Machado, L., Kittler, H., Vinterbo, S., Billhardt, H., Binder, M. (2001). “A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions,” Journal of Biomedical Informatics, 34, 28–36.PubMedCrossRefGoogle Scholar

  • Duda, R. O. and Hart, P. E. (1973). Pattern Classification and Scene Analysis, New York: John Wiley and Sons.Google Scholar

  • Dunham, M. H. (2002). Data Mining: Introductory and Advanced Topics, New Jersey, USA: Prentice Hall.Google Scholar

  • Efron, B. (1983). “Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation,” Journal of the American Statistical Association, 78(382), 316–330.CrossRefGoogle Scholar

  • Efron, B. and Tibshirani, R. (1993). An Introduction to the Bootstrap, Chapman and Hall.Google Scholar

  • Eisen, M., Spellman, P., Brown, P., and Botstein, D. (1998). “Cluster Analysis and Display of Genome-wide Expression Patterns,” in Proceedings of the National Academy of Sciences, 95, 14863–14868.CrossRefGoogle Scholar

  • Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. (1996). “From Data Mining to Knowledge Discovery in Databases,” AI Magazine, 17(3), 37–54.Google Scholar

  • Fisher, D. H. (1987). “Knowledge Acquisition via Incremental Conceptual Clustering,” Machine Learning, 2, 139–172.Google Scholar

  • Fogel, D. B. (1994). “An Introduction to Simulated Evolutionary Optimization,” IEEE Transactions on Neural Networks, 5, 3–14.CrossRefPubMedGoogle Scholar

  • Friedman, C. Hripcsak, G., Johnson, S. B., Cimino, J. J., Clayton, P. D. (1990). “A Generalized Relational Schema for an Integrated Clinical Patient Database,” in Proceedings of the 14thAnnual Symposium on Computer Applications in Medical Care, 335–339.Google Scholar

  • Friedman, C. and Hripcsak, G. (1998). “Evaluating Natural Language Processors in the Clinical Domain,” Methods of Information in Medicine, 37, 334–344.PubMedGoogle Scholar

  • Friedman, C., Kra, P., Yu, H., Krauthammer, M., and Rzhetsky, A. (2001). “GENIES: A Natural-language Processing System for the Extraction of Molecular Pathways from Journal Articles,” Bioinformatics, 17(Supp. 1), S74–S82.PubMedGoogle Scholar

  • Fukuda K., Tamura A., Tsunoda T., and Takagi T. (1998). “Toward Information Extraction: Identifying Protein Names from Biological Papers,” in Proceedings of the Pacific Symposium on Biocomputing, 707–718.Google Scholar

  • Fuller, S., Revere, D., Soderland, S., Bugni, P., Kadiyska, Y., Reber, L., Fuller, H., and Martin, G. (2002). “Modeling a Concept-Based Information System to Promote Scientific Discovery: The Telemakus System,” in Proceedings of the AMIA 2002 Annual Symposium, 1023.Google Scholar

  • Fuller, S., Revere, D., Bugni, P., Fuller, H., and Martin, G. (2004). “A Knowledgebase System to Enhance Scientific Discovery: Telemakus,” Biomedical Digital Libraries, 1(2–15).Google Scholar

  • Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Reading, MA: Addison-Wesley.Google Scholar

  • Golub T. R., Slonim D. K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J. P., Coller H., Loh M. L., Downing J. R., Caligiuri M. A., Bloomfield C. D., Lander E. S. (1999). “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring,” Science, 286(5439), 531–537.PubMedCrossRefGoogle Scholar

  • Gruninger, M. and Lee, J. (2002). “Ontology: Applications and Design,” Communications of the ACM, 45(2), 39–41CrossRefGoogle Scholar

  • Han, K., and Byun, Y. (2004). “Three-dimensional Visualization of Protein Interaction Networks,” Computers in Biology and Medicine, 34, 127–139.PubMedCrossRefGoogle Scholar

  • Harris, M. R., Savova, G. K., Johnson, T. M., and Chute, C. G. (2003). “A Term Extraction Tool for Expanding Content in the Domain of Functioning, Disability, and Health: Proof of Concept,” Journal of Biomedical Informatics, 36, 250–259.PubMedCrossRefGoogle Scholar

  • Hatzivassiloglou, V., Duboue, P. A., and Rzhetsky, A. (2001). “Disambiguating Proteins, Genes, and RNA in Text: A Machine Learning Approach,” Bioinformatics, 17(Supp. 1), S96–S106.Google Scholar

  • Hayes-Roth, F. and Jacobstein, N. (1994). “The State of Knowledge-based Systems,” Communications of the ACM, 37, 27–39.CrossRefGoogle Scholar

  • Hearst, M. A. (1999). “Untangling Text Data Mining,” in Proceedings of ACL’99: the 37th Annual Meeting of the Association for Computational Linguistics, Maryland, June 20–26.Google Scholar

  • Heathfield, H. and Louw, G. (1999). “New Challenges for Clinical Informatics: Knowledge Management Tools,” Health Informatics Journal, 5(2), 67–73.CrossRefGoogle Scholar

  • Herrero, J., Valencia, A., and Dopazo, J. (2001). “A Hierarchical Unsupervised Growing Neural Network for Clustering Gene Expression Patterns,” Bioinformatics, 17, 126–136.PubMedCrossRefGoogle Scholar

  • Hersh, W. (1996). Information Retrieval: A Health Care Perspective. Berlin, Germany: Springer-Verlag.Google Scholar

  • Hersh, W., Mailhot, M., Arnott-Smith, C., and Lowe, H. (2002). “Selective Automated Indexing of Findings and Diagnoses in Radiology Reports,” Journal of Biomedical Informatics, 34, 262–273.CrossRefGoogle Scholar

  • Herwig, R., Poustka, A., Müller, C., Bull, C., Lehrach, H., and O’Brien, J. (1999). “Large-scale Clustering of cDNA Fingerprinting Data,” Genome Research, 9, 1093–1105.PubMedCrossRefGoogle Scholar

  • Hirst, J. D. and Sternberg, M. J. E. (1992). “Prediction of Structural and Functional Features of Protein and Nucleic Acid Sequences by Artificial Neural Networks,” Biochemistry, 31, 7211–7218.PubMedCrossRefGoogle Scholar

  • Holland, J. H. (1975). Adaptation in Natural and Artificial Systems, Ann Arbor, MI: University of Michigan Press.Google Scholar

  • Hopfield, J. J. (1982). “Neural Network and Physical Systems with Collective Computational Abilities,” in Proceedings of the National Academy of Science, USA, 1982, 79(4), pp. 2554–2558.CrossRefGoogle Scholar

  • Houston, A. L., Chen, H., Hubbard, S. M., Schatz, B. R., Ng, T. D., Sewell, R. R. and Tolle, K. M. (1999). “Medical Data Mining on the Internet: Research on a Cancer Information System,” Artificial Intelligence Review, 13, 437–466.CrossRefGoogle Scholar

  • Hsu, A. L., Tang, S., and Halgamuge, S. K. (2003). “An Unsupervised Hierarchical Dynamic Self-organizing Approach to Cancer Class Discovery and Market Gene Identification in Microarray Data,” Bioinformatics, 19(16), 2131–2140.PubMedCrossRefGoogle Scholar

  • Hripcsak, G. (1993). “Monitoring the Monitor: Automated Statistical Tracking of a Clinical Event Monitor,” Computers and Biomedical Research, 26(5), 449–466.PubMedCrossRefGoogle Scholar

  • Hripcsak, G., Austin, J. H., Alderson, P. O., and Friedman, C. (2002). “Use of Natural Language Processing to Translate Clinical Information from a Database of 889,921 Chest Radiographic Reports,” Radiology, 224(1), 157–163.PubMedGoogle Scholar

  • Humphreys, B. L., Lindberg, D. A. B., and McCray, A. (1993). “The Unified Medical Language System,” Methods of Information in Medicine, 32(4), 281.PubMedGoogle Scholar

  • Humphreys, B. L., Lindberg, D. A. B., Schoolman, H. M., and Barnett, G. O. (1998). “The Unified Medical Language System: An Informatics Research Collaboration,” Journal of the American Medical Informatics Association, 5(1), 1–11.PubMedGoogle Scholar

  • Jackson, J. R. (2000). “The Urgent Call for Knowledge Management in Medicine,” The Physician Executive, 26(1), 28–31.Google Scholar

  • Jain, A. K., Dubes, R. C. and Chen, C. (1987). “Bootstrap Techniques for Error Estimation,” IEEE Transactions on Pattern Analysis and Machine Learning, 9(5), 628–633.CrossRefGoogle Scholar

  • Knirsch, C.A., Jain, N. L., Pablos-Mendez, A., Friedman, C., and Hripcsak, G. (1996). “Respiratory Isolation of Tuberculosis Patients Using Clinical Guidelines and an Automated Clinical Decision Support System,” Infection Control and Hospital Epidemiology, 19(2), 94–100.CrossRefGoogle Scholar

  • Jain, N. L. and Friedman, C. (1997). “Identification of Findings Suspicious for Breast Cancer Based on Natural Language Processing of Mammogram Reports.” in Proceedings of the Fall 1997 AMIA Conference, Philadelphia, USA, 829–833.Google Scholar

  • Janetzki, V., Allen, M., and Cimino, J. J. (2004). “Using Natural Language Processing to Link from Medical Text to On-line Information Resources,” Proceedings of Medinfo, 2004, 1665.Google Scholar

  • Joachims, T. (1998). “Text Categorization with Support Vector Machines: Learning with Many Relevant Features,” in Proceedings of the European Conference on Machine Learning, Berlin, 1998, pp. 137–142.Google Scholar

  • Kandaswamy, A., Kumar, C. S., Ramanathan, R. P. Jayaraman, R., and Malmurugan, N. (2004). “Neural Classification of Lung Sounds Using Wavelet Coefficients,” Computers in Biology and Medicine, 34, 523–537.PubMedCrossRefGoogle Scholar

  • Karasavvas, K. A., Baldock, R., and Burger, A. (2004). “Bioinformatics Integration and Agent Technology,” Journal of Biomedical Informatics, 37, 205–219.PubMedCrossRefGoogle Scholar

  • Kazama, J., Maino, T., Ohta, Y., and Tsujii, J. (2002). “Tuning Support Vector Machines for Biomedical Named Entity Recognition,” in Proceedings of the Workshop on Natural Language Processing in the Biomedical Domain, Philadelphia, USA, July 2002, 1–8.Google Scholar

  • Kohavi, R. (1995). “A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model Selection,” in Proceedings of the I4th International Joint Conference on Artificial Intelligence, San Francisco, CA, 1995, Morgan Kaufmann, pp. 1137–1143.Google Scholar

  • Kohonen, T. (1995). Self-organizing Maps, Springer-Verlag, Berlin.Google Scholar

  • Kononenko, I. (1993). “Inductive and Bayesian Learning in Medical Diagnosis,” Applied Artificial Intelligence, 7, 317–337, 1993.Google Scholar

  • Kovalerchuk, B., Vityaev, E., and Ruiz, J. F. (2001). “Consistent and Complete Data and ‘Expert’ Mining in Medicine,” in Cios, K. J. (Ed.), Medical Data Mining and Knowledge Discovery, New York, USA: Physica-Verlag.Google Scholar

  • Kretschmann, E., Fleischmann, W., and Apweiler, R. (2001). “Automatic Rule Generation for Protein Annotation with the C4.5 Data Mining Algorithm Applied on SWISS-PROT,” Bioinformatics, 17(10), 920–926.PubMedCrossRefGoogle Scholar

  • Krishnan, V. G. and Westhead, D. R. (2003). “A Comparative Study of Machine-Learning Methods to Predict the Effects of Single Nucleotide Polymorphisms on Protein Function,” Bioinformatics, 19(17), 2199–2209.

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