42 research outputs found
Reasoning under fuzzy vagueness and probabilistic uncertainty in the Semantic Web
Combining data from many different sources or from sources that are not entirely trusted brings challenges to the automated processing of such data. Knowledge presented in natural language is another challenge for computing. In the semantic web, many applications such as personal agents need to be able to manage multiple kinds of uncertainty. There are two main approaches to modeling uncertainty in the literature - fuzzy and probabilistic. These approaches model semantically different types of uncertainty. This paper focuses on approaches that combine both fuzzy and probabilistic reasoning in one framework to provide automated agents the capability to deal with both types of uncertainty
Main Concepts, State of the Art and Future Research Questions in Sentiment Analysis.
This article has multiple objectives. First of all, the fundamental concepts and challenges of the research ïŹeld known as Sentiment Analysis (SA) are presented. Secondly, a summary of a chronological account of the research performed in SA is provided as well as some bibliometric indicators that shed some light on the most frequently used techniques for addressing the central aspects of SA. The geographical locations of where the research took place are also given. In closing, it is argued that there is no hard evidence that fuzzy sets or hybrid approaches encompassing unsupervised learning, fuzzy sets and a solid psychological background of emotions could not be at least as effective as supervised learning techniques
Emergence and Evolution of Natural Languages: New Mathematical & Algorithmic Perspectives
In the search of new approaches to the problem of emergence and evolution of natural languages, Mathematics, Theoretical Computer Science, as well as Molecular Biology and Neuroscience, both deeply penetrated and profoundly inspired by concepts originated in Mathematics and Computer Science, represent today the richest pools of formal concepts, structures, and methods to borrow and to adapt
A Perception Based, Domain Specific Expert System for Question-Answering Support
The current search engine technologies mostly use a keyword based searching mechanism, which does not have any deductive abilities. There is an urgent need for a more intelligent question-answering system that will provide a more intuitive, natural language interface, and more accurate and direct search results. The introduction of Computing with Words (CwW) provides a new theoretical base for developing frameworks with support for dealing with information in natural language. This paper proposes a domain specific question-answering system based on Fuzzy Expert Systems using CwW. In order to perform the translation of natural language based information into a standard format for use with CwW, Probabilistic Context-Free Grammar is used