Name
Title | ||
|---|---|---|
Efficient Navigation in Learning Materials: An Empirical Study on the Linking Process. |
Abstract | ||
|---|---|---|
We focus on the task of linking topically related segments in a collection of documents. In this scope, an existing corpus of learning materials was annotated with links between its segments. Using this corpus, we evaluate clustering, topic models, and graph-community detection algorithms in an unsupervised approach to the linking task. We propose several schemes to weight the word co-occurrence graph in order to discovery word communities, as well as a method for assigning segments to the discovered communities. Our experimental results indicate that the graph-community approach might BE more suitable for this task. |
Year | Venue | Field |
|---|---|---|
2018 | AIED | Graph,Computer science,Artificial intelligence,Topic model,Cluster analysis,Empirical research,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Mota | 1 | 2 | 2.15 |
| Luísa Coheur | 2 | 199 | 34.38 |
| Maxine Eskenazi | 3 | 979 | 127.53 |