RESUMEN

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.

RESUMEN

With the advent of the Internet of Things, billions of objects or devices are inserted into the global computer network, generating and processing data at a volume never imagined before. This paper proposes a way to collect and process local data through a data fusion technology called summarization. The main feature of the proposal is the local data fusion, through parameters provided by the application, ensuring the quality of data collected by the sensor node. In the evaluation, the sensor node was compared when performing the data summary with another that performed a continuous recording of the collected data. Two sets of nodes were created, one with a sensor node that analyzed the luminosity of the room, which in this case obtained a reduction of 97% in the volume of data generated, and another set that analyzed the temperature of the room, obtaining a reduction of 80% in the data volume. Through these tests, it has been proven that the local data fusion at the node can be used to reduce the volume of data generated, consequently decreasing the volume of messages generated by IoT environments.

RESUMEN

Describimos la aplicación de la tecnología de procesamiento de lenguaje natural (NLP) al análisis del lenguaje subjetivo. En particular, nos concentramos en la problemática de la clasificación de opinión de material textual extraído de fuentes de datos relacionados con negocios. Estudiamos la derivación de los valores de opiniones de palabras a partir del recurso léxico SentiWordNet y utilizamos estos valores para la interpretación de texto con el objetivo de obtener la valoración de una opinión a partir de sus palabras y frases. Utilizamos características de las palabras para inducir un clasificador basado en el uso de Máquinas de Vectores de Soporte que alcanzan resultados acordes con el estado del arte. También mostramos experimentos preliminares en los que el uso de resúmenes de opiniones ofrece ventaja competitiva para el problema de clasificación respecto del uso de documentos completos cuando los documentos son extensos y contienen material tanto subjetivo como no-subjetivo.(AU)

We describe the application of natural language processing (NLP) technology to the analysis of subjective language. In particular we concentrate on the problem of opinion classification of textual material extracted from business-related data-sources. We study the derivation of sentiment values for words from the SentiWordNet lexicalresource and use them for text interpretation to produce word, sentence, and text based sentiment features for opinion classification. We use word-based and sentiment basedfeatures to induce a classifier based on the use of Support Vector Machinesachieving state of the art results. We also show preliminary experiments where the use of summaries before opinion classification provides competitive advantage over the use of full documents when the documents are long and contain both subjective andnon-subjective material.(AU)

Asunto(s)

RESUMEN

Describimos la aplicación de la tecnología de procesamiento de lenguaje natural (NLP) al análisis del lenguaje subjetivo. En particular, nos concentramos en la problemática de la clasificación de opinión de material textual extraído de fuentes de datos relacionados con negocios. Estudiamos la derivación de los valores de opiniones de palabras a partir del recurso léxico SentiWordNet y utilizamos estos valores para la interpretación de texto con el objetivo de obtener la valoración de una opinión a partir de sus palabras y frases. Utilizamos características de las palabras para inducir un clasificador basado en el uso de Máquinas de Vectores de Soporte que alcanzan resultados acordes con el estado del arte. También mostramos experimentos preliminares en los que el uso de resúmenes de opiniones ofrece ventaja competitiva para el problema de clasificación respecto del uso de documentos completos cuando los documentos son extensos y contienen material tanto subjetivo como no-subjetivo.

We describe the application of natural language processing (NLP) technology to the analysis of subjective language. In particular we concentrate on the problem of opinion classification of textual material extracted from business-related data-sources. We study the derivation of sentiment values for words from the SentiWordNet lexicalresource and use them for text interpretation to produce word, sentence, and text based sentiment features for opinion classification. We use word-based and sentiment basedfeatures to induce a classifier based on the use of Support Vector Machinesachieving state of the art results. We also show preliminary experiments where the use of summaries before opinion classification provides competitive advantage over the use of full documents when the documents are long and contain both subjective andnon-subjective material.

Asunto(s)