SalienceGraph: Visualizing Salience Dynamics of Written Discourse by Using Reference Probability and PLSA

Authors:
Shun Shiramatsu;Kazunori Komatani;Tetsuya Ogata;Hiroshi G. Okuno
Affiliations:
Graduate School of Informatics, Kyoto University,;Graduate School of Informatics, Kyoto University,;Graduate School of Informatics, Kyoto University,;Graduate School of Informatics, Kyoto University,
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

Citing 7
Cited 1

Centering: a framework for modeling the local coherence of discourse

Computational Linguistics
Designing the User Interface: Strategies for Effective Human-Computer Interaction

Designing the User Interface: Strategies for Effective Human-Computer Interaction
Bursty and hierarchical structure in streams

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Latent dirichlet allocation

The Journal of Machine Learning Research
Japanese dependency analysis using cascaded chunking

COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Topic-based language models using Dirichlet Mixtures

Systems and Computers in Japan
Meaning games

JSAI'07 Proceedings of the 2007 conference on New frontiers in artificial intelligence

System for supporting web-based public debate using transcripts of face-to-face meeting

IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

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.