Classifying literary genres: a methodological synergy of computational modelling and lexical semantics / Classificação de gêneros literários: uma sinergia metodológica de modelagem computacional e semântica

Abdulfattah Omar


ABSTRACT: Classifying literary genres has always been methodologically confined to philological methods and what is commonly known as Vector Space Clustering (VSC). The problem has been exasperated with the widening gap between computational theory and traditional analysis of literary texts. Towards finding a solution to this problem, the current study utilizes a synergetic approach that brings together two established methods. First, a computational model of genre classification is drawn upon for identifying concept-based, rather than word-bound, topics, where the representation of texts is secured via the ‘bag of concepts’ (BOC) model as well as the sense-restricted knowledge and meaningful links holding between and among concepts; relatedly, the two model strands of explicit semantic analysis (ESA) and ConceptNet have enacted text classification. Second, a contextual lexical semantic approach (CRUSE, 1986, 2000) is employed so that the contextual variability of word meanings and concepts can be tackled within the confines of the target literary genres classified. The findings of present study have shown that the current composite approach of computational and semantic models has resulted in improved performance in classifying literary genres, especially with respect to delineating the links between each cluster’s document-members and generalizing about their unifying genre. Further implications have emerged from the present study, namely, the benefits reserved for digital libraries and the process of archiving, where literary-text classification has proven problematic to both users and readers in many cases.

KEYWORDS: bag of concepts (BOC); ConceptNet; Explicit Semantic Analysis (ESA); genre classification; topic concepts; Vector Space clustering (VSC).


RESUMO: A classificação de gêneros literários sempre se restringiu metodologicamente aos métodos filológicos e ao que é comumente conhecido como Vector Space Clustering (VSC). O problema foi exasperado com a crescente lacuna entre a teoria computacional e a análise tradicional de textos literários. Para encontrar uma solução para esse problema, o presente estudo utiliza uma abordagem sinérgica que reúne dois métodos estabelecidos. Primeiro, um modelo computacional de classificação de gênero é utilizado para identificar tópicos baseados em conceito, em vez de vinculados a palavras, em que a representação de textos é protegida por meio do modelo “bolsa de conceitos” (BOC), bem como o conhecimento restrito aos sentidos e os vínculos significativos entre os conceitos; De maneira semelhante, os dois modelos de análise semântica explícita (ASE) e ConceptNet promulgaram a classificação do texto. Segundo, uma abordagem semântica lexical contextual (CRUSE, 1986, 2000) é empregada para que a variabilidade contextual dos significados e conceitos das palavras possa ser abordada dentro dos limites dos gêneros literários alvo classificados. As descobertas do presente estudo mostraram que a atual abordagem composta de modelos computacionais e semânticos resultou em melhor desempenho na classificação de gêneros literários, especialmente no que diz respeito a delinear os vínculos entre os membros do documento de cada grupo e generalizar sobre seu gênero unificador. Outras implicações emergiram do presente estudo, a saber, os benefícios reservados para as bibliotecas digitais e o processo de arquivamento, em que a classificação de textos literários se mostrou problemática para usuários e leitores em muitos casos.


PALAVRAS-CHAVE: bolsa de conceitos (COB); ConceptNet; Análise Semântica Explícita (ASE); classificação de gênero; conceitos de tópicos; Vector Space Clustering (VSC).


bag of concepts (BOC); ConceptNet; Explicit Semantic Analysis (ESA); genre classification; topic concepts; Vector Space clustering (VSC).

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Texto Livre: Linguagem e Tecnologia
ISSN 1983-3652 (eletrônica)

Faculdade de Letras da Universidade Federal de Minas Gerais

Belo Horizonte - Minas Gerais (Brasil)

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