TY - GEN
T1 - SalienceGraph
T2 - 10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008
AU - Shiramatsu, Shun
AU - Komatani, Kazunori
AU - Ogata, Tetsuya
AU - Okuno, Hiroshi G.
PY - 2008/12/1
Y1 - 2008/12/1
N2 - 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.
AB - 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.
KW - Discourse analysis
KW - Discourse salience
KW - PLSA
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=58349099072&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58349099072&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-89197-0_83
DO - 10.1007/978-3-540-89197-0_83
M3 - Conference contribution
AN - SCOPUS:58349099072
SN - 354089196X
SN - 9783540891963
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 890
EP - 902
BT - PRICAI 2008
Y2 - 15 December 2008 through 19 December 2008
ER -