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.
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Notes
- 1.
Source code and annotated corpus available at: https://github.com/pjdrm/SegmentLinkingAVL.
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Acknowledgements
This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2013; also under projects LAW-TRAIN (H2020-EU.3.7, contract 653587), and INSIDE (CMUP-ERI/HCI/0051/2013), and also through the Carnegie Mellon Portugal Program under Grant SFRH/BD/51917/2012.
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Mota, P., Coheur, L., Eskenazi, M. (2018). Efficient Navigation in Learning Materials: An Empirical Study on the Linking Process. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_42
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DOI: https://doi.org/10.1007/978-3-319-93846-2_42
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