Abstract
Since public involvement in the decision-making process for community development needs a lot of efforts and time, support tools for speeding up the consensus building process among stakeholders are required. This paper presents a new method for finding, tracking and visualizing participants’ concerns (topics) from the record of a public debate. For finding topics, we use the salience of a term, which is computed as its reference probability based on referential coherence in the Centering Theory. Our system first annotates a debate record or minute into Global Document Annotation (GDA) format automatically, and then computes the salience of each term from the GDA-annotated text sentence by sentence. Then, by using the Probalilistic Latent Semantic Analytsis (PLSA), our system reduces the dimensions of the vector of salience values of terms into a set of major latent topics. For tracking topics, we use the salience dynamics, which is computed as the temporal change of joint attention to the major latent topics with additional user-supplied terms. The resulting graph is called SalienceGraph. For visualizing SalienceGraph, we use 3D visualizer with GUI designed by “overview first, zoom and filter, then details on demand” principle. SalienceGraph provides more accurate trajectory of topics than conventional TF·IDF.
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References
Jeong, H., Hatori, T., Kobayashi, K.: Discourse analysis of public debates: A corpus-based approach. In: Proceedings of 2007 IEEE International Conference on Systems, Man and Cybernetics (SMC 2007), pp. 1782–1793 (2007)
Shneiderman, B.: Designing the User Interface: Strategies for Effective Human-Computer Interaction, 3rd edn. Pearson Addison Wesley (1998)
Hasida, K.: Global Document Annotation (GDA), http://i-content.org/GDA/
Kudo, T., Matsumoto, Y.: Japanese dependency analysis using cascaded chunking. In: Proceeding of the 6th conference on Natural language learning (CoNLL-2002, COLING 2002 Post-Conference Workshops), pp. 1–7 (2002)
Grosz, B., Joshi, A., Weinstein, S.: Centering: A Framework for Modeling the Local Coherence of Discourse. Computational Linguistics 21(2), 203–226 (1995)
Hasida, K., Shiramatsu, S., Komatani, K., Ogata, T., Okuno, H.G.: Meaning games. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) JSAI 2007. LNCS (LNAI), vol. 4914, pp. 228–241. Springer, Heidelberg (2008)
Murdock, B.: The Serial Position Effect in Free Recall. Journal of Experimental Psychology 64, 482–488 (1962)
Maekawa, K.: Corpus of Spontaneous Japanese: Its Design and Evaluation. In: Proceedings of the ISCA & SSPR 2003, pp. 7–12 (2003)
GSK (Gengo Shigen Kyokai): Linguistic resourse catalogue (in Japanese), http://www.gsk.or.jp/catalog.html
YodoRiver-Watershed-Committee: Minute of the Debate Session among Citizens and Committee Members (Nyu Dam) (2005) (in Japanese), http://www.yodoriver.org/kaigi/biwa/17.html#ikenkoukan
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Sadamitsu, K., Mishina, T., Yamamoto, M.: Topic-based language models using dirichlet mixtures. Systems and Computers in Japan 38(12), 76–85 (2007)
Mochihashi, D., Matsumoto, Y.: Context as filtering. In: Advances in Neural Information Processing Systems (NIPS 2005), vol. 18, pp. 907–914 (2006)
Kleinberg, J.: Bursty and hierarchical structure in streams. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and DataMining, pp. 1–25 (2002)
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Shiramatsu, S., Komatani, K., Ogata, T., Okuno, H.G. (2008). SalienceGraph: Visualizing Salience Dynamics of Written Discourse by Using Reference Probability and PLSA. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_83
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DOI: https://doi.org/10.1007/978-3-540-89197-0_83
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