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Semantic aspects in the representation of texts for automatic classification

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Roberta Akemi Sinoara
Total Authors: 1
Document type: Doctoral Thesis
Press: São Carlos.
Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação
Defense date:
Examining board members:
Solange Oliveira Rezende; Sandra Maria Aluisio; Frederico Luiz Gonçalves de Freitas; Leandro Nunes de Castro Silva
Advisor: Solange Oliveira Rezende

Text Mining applications are numerous and varied since a huge amount of textual data are created daily. The quality of the final solution of a Text Mining process depends, among other factors, on the adopted text representation model. Despite the fact that syntactic and semantic relations influence natural language meaning, traditional text representation models are limited to words. The use of such models does not allow the differentiation of documents that use the same vocabulary but present different ideas about the same subject. The motivation of this work relies on the diversity of text classification applications, the potential of vector space model representations and the challenge of dealing with text semantics. Having the general purpose of advance the field of semantic representation of documents, we first conducted a systematic mapping study of semantics-concerned Text Mining studies and we categorized classification problems according to their semantic complexity. Then, we approached semantic aspects of texts through the proposal, analysis, and evaluation of seven text representation models: (i) gBoED, which incorporates text semantics by the use of domain expressions; (ii) Uni-based, which takes advantage of word sense disambiguation and hypernym relations; (iii) SR-based Terms and SR-based Sentences, which make use of semantic role labels; (iv) NASARIdocs, Babel2Vec and NASARI+Babel2Vec, which take advantage of word sense disambiguation and embeddings of words and senses.We analyzed the expressiveness and interpretability of the proposed text representation models and evaluated their classification performance against different literature models. While the proposed models gBoED, Uni-based, SR-based Terms and SR-based Sentences have improved expressiveness, the proposals NASARIdocs, Babel2Vec and NASARI+Babel2Vec are latently enriched by the embeddings semantics, obtained from the large training corpus. This property has a positive impact on text classification performance. (AU)

FAPESP's process: 13/14757-6 - Incorporating the semantics into the websensors construction process
Grantee:Roberta Akemi Sinoara
Support type: Scholarships in Brazil - Doctorate