Mapping text documents in an LDA-based topic-space is a classical way to extract high-level representation of text documents. Unfortunately, LDA is highly sensitive to hyper-parameters related to the number of classes, or word and topic distribution, and there is no systematic way to pre-estimate optimal configurations. Moreover, various hyper-parameter configurations offer complementary views on the document. In this paper, we propose a method based on a two-step process that, first, expands the representation space by using a set of topic spaces and, second, compacts the representation space by removing poorly relevant dimensions. These two steps are based respectively on multi-view LDA-based representation spaces and factor-analysis models. This model provides a view-independent representation of documents while extracting complementary information from a massive multi-view representation. Experiments are conducted on the DECODA conversation corpus and the Reuters-21578 textual dataset. Results show the efficiency of the proposed multiview compact representation paradigm. The proposed categorization system reaches an accuracy of 86.5% with automatic transcriptions of conversations from DECODA corpus and a Macro-F1 of 80% during a classification task of the well-known Reuters-21578 corpus, with a significant gain compared to the baseline (best single topic space configuration), as well as methods and document representations previously studied.
['Mohamed Morchid', 'Mohamed Bouallegue', 'Richard Dufour', 'Georges Linarès', 'Driss Matrouf', 'Renato De Mori']
Compact multiview representation of documents based on the total variability space