Section: New Results

Mining Texts at discourse level

Linguistic discourse refers to the meaning of large chunks of text, from phrases to whole documents. It could be very useful for guiding attempts at text mining, which focus on document selection, document summarization, or other knowledge extraction goals. Hence the aim of this work is to apply Knowledge Discovery in Databases (KDD) methods to texts annotated with discourse information. Maxime Amblard with Yannick Toussaint (Orpailleur team) and Sara van de Moosdijk (master 2 intern) approach the problem by extracting discourse relations using unsupervised methods, which are then used to construct a knowledge model with Formal Concept Analysis (FCA). Pattern Structures (PS), an advancement in FCA, allow for the modelling of complex data. Our method is applied to a corpus of medical articles compiled from PubMed. This medical data is enhanced with concepts from the UMLS MetaThesaurus combined with the UMLS Semantic Network to serve as an ontology for Pattern Structure classification. The results show that despite having a large amount of noise, the method is promising and could be applied to other domains than the medical domain. We explore the pitfalls and suggest ways in which the process could be improved (Submission under review).