A method for multilingual text mining and retrieval using growing hierarchical self-organizing maps

Abstract

With the increasing number of multilingual texts in the internet, multilingual text retrieval techniques have become an important research issue. However, the discovery of relationships between different languages remains an open problem. In this paper we propose a method, which applies the growing hierarchical self-organizing map (GHSOM) model, to discover knowledge from multilingual text documents. Multilingual parallel corpora were trained by the GHSOM to generate hierarchical feature maps. A discovery process is then applied on these maps to discover the relationships between documents of different languages. The relationships between keywords of different languages are also revealed. We conducted experiments on a set of Chinese—English bilingual parallel corpora to discover the relationships between documents of these languages. We also use such relationships to perform multilingual information retrieval tasks. The experimental results show that our multilingual text mining approach may capture conceptual relationships among documents as well as keywords written in different languages.

Organización de documentos me diante grafos de relaciones semánticas

“…Para ello trasladan las palabras de la consulta a conceptos. Por otro lado, las redes neuronales SOM (Self-Organized Maps) [5] han sido aplicadas exitosamente en varios trabajos sobre organización y búsqueda de documentos [6,7,8,9,10,11]. Por ejemplo, Fernandes y Ludermir [12] propusieron un método de extracción de características para representación de documentos relacionados mediante redes neuronales auto-organizadas.…”

“…Para ello se genera una matriz de contingencia que denota la ocurrencia de un sustantivo con los diferentes verbos con los que está relacionado en el texto. De las relaciones sustantivo-verbo se obtienen los lexemas para ambos, quedando una relación de lexema-de-sustantivo -lexema-de-verbo 9 . Las relaciones sustantivo-verbo se representan siguiendo la idea de las matrices palabra-documento del Modelo Espacio Vectorial (MEV) tomando en cuenta el peso de dichas relaciones, el peso se calcula mediante TF-IDF [33].…”

An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection

“…Also, Chinese text has no delimiters to mark word boundaries, while English text uses a space as word delimiter. Several methods were proposed to deal with Chinese text [29]- [32], but they are not efficient or sufficiently robust to process research proposals.…”

Decision Support for E-Governance: A Text Mining Approach

“…Documents written in different languages were first clustered and organized into hierarchies using the growing hierarchical self-organizing map model. They have also noted that in the domain of multilingual text mining, little attention has to be paid for building multilingual document hierarchies and deriving associations from such hierarchies [48]. Rowena Chau et al (2004), have discussed about the multilingual text mining approach to cross-lingual text retrieval (CLTR), and their multilingual text mining approach for automatically discovering the multilingual linguistic knowledge contributes to cross-lingual text retrieval by providing a more affordable alternative to the costly manually constructed linguistic resources.…”