Linguistic Processing in Lattice-Based Taxonomy Construction

Building a lattice-based taxonomy over a text corpus with formal concept analysis (FCA) methods requires preliminary text processing that would enable construction of a context. We consider several natural language processing methods aimed at automatic attribute acquisition from texts. In particular, we derive attributes of three types: frequent words, latent topics and named entities. Afterwards, we construct a context for each type taking documents in the corpus as a set of objects. Then the corresponding concept lattices are built and pruned with the help of stability index in order to improve the readability of the diagrams. The proposed technique is illustrated on a collection of 26 texts in English dealing with political domain. In this case, the technique serves as a tool for deeper understanding of the interests of different political actors producing political texts by clarifying the connections between notions they use in them.