Abstract
A simple document search is insufficient when we analyse corporate information. Not only a list of search results, but also a justification why the results match the query condition is important. This paper proposes a method to extract cause of bankruptcy from news articles applying the co-occurrence analysis of words.
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References
Baba, T., Liu, L., Hirokawa, S.: Formal Concept Analysis of Medical Incident Reports. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010. LNCS, vol. 6278, pp. 207–214. Springer, Heidelberg (2010)
Aoshima, T., Fukuta, N., Yokoyama, S., Ishikawa, H.: A Proposal of Constrained Clustering of Micro-Blogs. In: DEIM 2010 B1-3 (2010) (in Japanese)
Carpineto, C., Romano, G.: Concept Data Analysis Theory and Application. John Wiley and Sons, Chichester (2004)
Egoshi, R., Nagai, H., Nakamura, T.: Extraction of Important Articles from Related Articles using Small World Structure. IPSJ SIG. Notes (113), 17–22 (2008) (in Japanese)
Ganter, G., Wille, R., Franzke, C.: Formal Concept Analysis Mathematical Foundation. Springer, Heidelberg (1999)
Izumi, K., Goto, T., Matsui, T.: Analysis of Financial Markets Fluctuation by Textual Information. Journal of JSAI 25(3), 383–387 (2010) (in Japanese)
Kishida, K.: Property of Mean Average Precision as Performance Measure in Retrieval Experiment. IPSJ SIG. Notes (74), 97–104 (2001) (in Japanese)
Shirata, C.Y., Takeuchi, H., Ogino, S., Watanabe, H.: Financial Analysis using Text Mining Technique: Empirical Analysis of Bankrupt Companies. Business Analysis Association Annual Report (25), 40–47 (2009) (in Japanese)
Chan, S.W.K.: Extraction of salient textual patterns: Synergy between lexical cohesion and contextual coherence. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 34(2), 205–218 (2004)
Chuang, W.T., Yang, J.: Extracting sentence segments for text summarization: A machine learning approach. SIGIR Forum (ACM Special Interest Group on Information Retrieval), 152–159 (2000)
Goda, S., Ohsawa, Y.: Chance discovery in credit risk management -Time order method and directed KeyGraph for estimation of chain reaction bankruptcy structure. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) JSAI 2007. LNCS (LNAI), vol. 4914, pp. 247–254. Springer, Heidelberg (2008)
Huff, A.S. (ed.): Mapping Strategic Thought. Wiley, Chichester (1990)
Iino, Y., Hirokawa, S.: Time Series Analysis of R and D Team Using Patent Information. In: Velásquez, J.D., RÃos, S.A., Howlett, R.J., Jain, L.C. (eds.) KES 2009. LNCS, vol. 5712, pp. 464–471. Springer, Heidelberg (2009)
Kida, M.: Cognitive research of Asahi ’s organizational renewal -textminig of annual reports organizational science. Academic Journal 39(4) (2006)
Koester, B.: Conceptual Knowledge Retrieval with FooCA: Improving Web Search Engine Results with Contexts anc Concept Hierarchies. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 176–190. Springer, Heidelberg (2006)
Li, B., Zhou, L., Fen, S., Wong, K.-F.: A Unified Graph Model for Sentence-based Opinion Retrieval. In: Proc. 48th ACL, pp. 1367–1375 (2010)
Li, H., Sun, J., Wu, J.: Predicting business failure using classification and regression tree: An empirical comparison with popular classifical statistical methods and top classification mining methods. Expert Systems with Applications 37(8), 5895–5904 (2010)
Liu, X., Webster, J., Kit, C.: An extractive text summarizer based on significant words. In: Li, W., Mollá-Aliod, D. (eds.) ICCPOL 2009. LNCS (LNAI), vol. 5459, pp. 168–178. Springer, Heidelberg (2009)
Ouyang, Y., Li, W., Wei, F., Lu, Q.: Learning similar ity functions in graph-based document summarization. In: Li, W., Mollá-Aliod, D. (eds.) ICCPOL 2009. LNCS (LNAI), vol. 5459, pp. 189–200. Springer, Heidelberg (2009)
Mining System based on Search Engine and Concept Graph for Large-Scale Financial Report Texts. In: Proc. 2nd IEEE ICIFE, pp. 675–679 (2010)
Shimoji, Y., Wada, T., Hirokawa, S.: Dynamic Thesaurus Construction from English- Japanese Dictionary. In: Proc. The Second International Conference on Complex, Intelligent and Software Intensive Systems, pp. 918–923 (2008)
Shin, K.-S., Lee, T.-S., Kim, H.-J.: An application of support vector machines in bankruptcy prediction model. Expert Systems with Application 28(1), 127–135 (2005)
Yamamoto, Y., Orihara, R.: Keyword Extraction using the Word Co-occurrence Network Properties that is Independent of Languages and Document Types and Its Evaluation by Prediction of Headline Words. Trans. JSAI 24(3), 303–312 (2009) (in Japanese)
Takeuchi, H., Ogino, S., Watanabe, H., Shirata, Y.: Context-based text mining for insights in long documents. In: Yamaguchi, T. (ed.) PAKM 2008. LNCS (LNAI), vol. 5345, pp. 123–134. Springer, Heidelberg (2008)
Tsai, C.-F.: Feature selection in bankruptcy prediction. Knowledge-Based Systems 22, 120–127 (2009)
Turney, P.D.: Learning algorithms for keyphrase extraction. Information Retrieval 2(4), 303–336 (2000)
Uchida, Y., Yoshikawa, T., Furuhashi, T., Hirao, E., Iguchi, H.: Extraction of important keywords in free text of questionnaire data and visualization of relationship among sentences. In: IEEE International Conference on Fuzzy Systems, art. no. 5277332, pp. 1604–1608 (2009)
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Hirokawa, S., Baba, T., Nakatoh, T. (2011). Search and Analysis of Bankruptcy Cause by Classification Network. In: Bellatreche, L., Mota Pinto, F. (eds) Model and Data Engineering. MEDI 2011. Lecture Notes in Computer Science, vol 6918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24443-8_17
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DOI: https://doi.org/10.1007/978-3-642-24443-8_17
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