Aplicación de tecnologías de procesamiento de lenguaje natural y tecnología semántica en Brand Rain y Anpro21

Este artículo presenta la aplicación y resultados obtenidos de la investigación en técnicas de procesamiento de lenguaje natural y tecnología semántica en Brand Rain y Anpro21. Se exponen todos los proyectos relacionados con las temáticas antes mencionadas y se presenta la aplicación y ventajas de la transferencia de la investigación y nuevas tecnologías desarrolladas a la herramienta de monitorización y cálculo de reputación Brand Rain.

Detecting implicit expressions of affect in text using EmotiNet and its extensions

In the past years, an important volume of research in Natural Language Processing has concentrated on the development of automatic systems to deal with affect in text. The different approaches considered dealt mostly with explicit expressions of emotion, at word level. Nevertheless, expressions of emotion are often implicit, inferrable from situations that have an affective meaning. Dealing with this phenomenon requires automatic systems to have “knowledge” on the situation, and the concepts it describes and their interaction, to be able to “judge” it, in the same manner as a person would. This necessity motivated us to develop the EmotiNet knowledge base — a resource for the detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence. In this article, we briefly present the process undergone to build EmotiNet and subsequently propose methods to extend the knowledge it contains. We further on analyse the performance of implicit affect detection using this resource. We compare the results obtained with EmotiNet to the use of alternative methods for affect detection. Following the evaluations, we conclude that the structure and content of EmotiNet are appropriate to address the automatic treatment of implicitly expressed affect, that the knowledge it contains can be easily extended and that overall, methods employing EmotiNet obtain better results than traditional emotion detection approaches.

Computer science research at Langley : proceedings of a workshop held at Langley Research Center, November 2-5-, 1981 /

Subject Category 59.

Projects in the hardware research group of the department of computer science of the University of Illinois,

Illustrations, p. 30-52, numbered as leaves.

1979 SIGNUM Meeting on Numerical Ordinary Differential Equations, April 3-5, 1979, University Inn, Champaign, Illinois : jointly sponsored by Association for Computing Machinery, Special Interest Group on Numerical Mathematics; Department of Computer Science, University of Illinois at Urbana-Champaign; National Science Foundation /

Includes bibliographical references.

The graduate program in computer science.

Bibliography: p. 7-9.

Syllabus for low level performance for the qualifying examination in computer science /

"April, 1970"

ACSES : the automated computer science education system at the University of Illinois /

Bibliography: p. 141-143.

Record of achievements and plans of the Information Engineering Laboratory in the Department of Computer Science of the University of Illinois at Urbana-Champaign, 1955-1973

Mode of access: Internet.

ROBOCAR : educating the layman in computer science /

Originally presented as the author's thesis (M.A.), University of Illinois at Urbana-Champaign.

Information extraction from clinical and radiology notes for liver cancer staging

Thesis (Ph.D.)--University of Washington, 2016-06

Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping

The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.