Name
Abstract | ||
|---|---|---|
As the corpora of online tutoring sessions grow by orders of magnitude, dialogue act classification can be used to capture increasingly fine-grained details about events during tutoring. In this paper, we apply machine learning to build models that can classify 133 (126 defined acts plus 7 to represent unknown and undefined acts) possible dialogue acts in tutorial dialog from online tutoring services. We use a data set of approximately 95000 annotated utterances to train and test our models. Each model was trained to predict top level Dialogue Acts using several learning algorithms. The best learning algorithm from top level Dialogue Acts was then applied to learn subcategories which was then applied in multi-level classification. |
Year | Venue | Field |
|---|---|---|
2015 | EDM | Dialog box,Online tutoring,Dialogue acts,Computer science,Natural language processing,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 1 | 0.37 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Borhan Samei | 1 | 49 | 8.97 |
| Vasile Rus | 2 | 973 | 134.69 |
| Benjamin D. Nye | 3 | 57 | 10.62 |
| Donald Morrison | 4 | 6 | 2.90 |