Estrategia para la integración de enfoques para la extracción y enlazado de entidades nombradas

Abstract

The extraction of named entities and their linking to a Semantic Web knowledge base is a task whose results support the process of extracting potentially useful elements of information from unstructured text. In the last decade, this task has been widely addressed with the aim of making possible the interconnection, exchange, and query of data on the Semantic Web. In this sense, several approaches based on the idea of ensemble methods (like those in Machine Learning) have been proposed to combine distinct named entity extraction and linking techniques in order to get better results than using a single such technique. Although the idea is to exploit features provided by diverse approaches to extract and link named entities from text, there are some issues to solve for integrating their results (e.g., heterogeneous output, duplicated entities). In this paper, we propose a strategy to integrate the output provided by some entity extraction and linking tools in an ensemble-like scheme. For such purpose, we consider steps for collecting and merging results supported by filtering decisions to overcome issues such as duplicated and/or overlapped entities. The results showed an increased performance in terms of the F-measure compared to isolated approaches.