TY - GEN
T1 - Using graphical models to classify dialogue transition in online Q&A discussions
AU - Seo, Soo Won
AU - Kang, Jeon Hyung
AU - Drummond, Joanna
AU - Kim, Jihie
PY - 2011
Y1 - 2011
N2 - In this paper, we examine whether it is possible to automatically classify patterns of interactions using a state transition model and identify successful versus unsuccessful student Q&A discussions. For state classification, we apply Conditional Random Field and Hidden Markov Models to capture transitions among the states. The initial results indicate that such models are useful for modeling some of the student dialogue states. We also show the results of classifying threads as successful/unsuccessful using the state information.
AB - In this paper, we examine whether it is possible to automatically classify patterns of interactions using a state transition model and identify successful versus unsuccessful student Q&A discussions. For state classification, we apply Conditional Random Field and Hidden Markov Models to capture transitions among the states. The initial results indicate that such models are useful for modeling some of the student dialogue states. We also show the results of classifying threads as successful/unsuccessful using the state information.
KW - Q&A discussion classification
KW - Student online discussions
UR - http://www.scopus.com/inward/record.url?scp=79959306506&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-21869-9_98
DO - 10.1007/978-3-642-21869-9_98
M3 - Conference contribution
AN - SCOPUS:79959306506
SN - 9783642218682
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 550
EP - 553
BT - Artificial Intelligence in Education - 15th International Conference, AIED 2011
T2 - 15th International Conference on Artificial Intelligence in Education, AIED 2011
Y2 - 28 June 2011 through 1 July 2011
ER -