Abstract
As previous chapters were overall oriented towards comprehension and productions from the perspectives of individual and collaborative learning, this chapter is focused on presenting automatic discourse analysis models and natural language processing techniques that ground a computational and quantifiable perspective of cohesion and coherence and that greatly impact the underlying functionalities of our developed systems (A.S.A.P., Ch.A.M.P., PolyCAFe and ReaderBench).
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Keywords
- Semantic Similarity
- Latent Dirichlet Allocation
- Latent Semantic Analysis
- Semantic Distance
- Word Sense Disambiguation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Dascalu, M. (2014). Computational Discourse Analysis. In: Analyzing Discourse and Text Complexity for Learning and Collaborating. Studies in Computational Intelligence, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-03419-5_4
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