Fuzzy Believer

Believe It or Not: Solving the TAC 2009 Textual Entailment Tasks through an Artificial Believer System

Abstract

The Text Analysis Conference (TAC) 2009 competition featured a new textual entailment search task, which extends the 2008 textual entailment task. The goal is to find information in a set of documents that are entailed from a given statement. Rather than designing a system specifically for this task, we investigated the adaptation of an existing artificial believer system to solve this task. The results show that this is indeed possible, and furthermore allows to recast the existing, divergent tasks of textual entailment and automatic summarization under a common umbrella.

A Belief Revision Approach to Textual Entailment Recognition

Abstract

An artificial believer has to recognize textual entailment to categorize beliefs. We describe our system – the Fuzzy Believer system – and its application to the TAC/RTE three-way task.

Minding the Source: Automatic Tagging of Reported Speech in Newspaper Articles


Abstract

Reported speech in the form of direct and indirect reported speech is an important indicator of evidentiality in traditional newspaper texts, but also increasingly in the new media that rely heavily on citation and quotation of previous postings, as for instance in blogs or newsgroups. This paper details the basic processing steps for reported speech analysis and reports on performance of an implementation in form of a GATE resource.

Creating a Fuzzy Believer to Model Human Newspaper Readers

Montreal 2007

Abstract

We present a system capable of modeling human newspaper readers. It is based on the extraction of reported speech, which is subsequently converted into a fuzzy theory-based representation of single statements. A domain analysis then assigns statements to topics. A number of fuzzy set operators, including fuzzy belief revision, are applied to model different belief strategies. At the end, our system holds certain beliefs while rejecting others.

Processing of Beliefs extracted from Reported Speech in Newspaper Articles

A fuzzy believer?

Abstract

The growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of our artificial believer system presented in this paper is to extract and analyze statements of opinion from newspaper articles.

Beliefs are modeled using a fuzzy-theoretic approach applied after NLP-based information extraction. A fuzzy believer models a human agent, deciding what statements to believe or reject based on different, configurable strategies.

Fuzzy Belief Revision

Abstract

Toulouse
Fuzzy sets, having been the long-standing mainstay of modeling and manipulating imperfect information, are an obvious candidate for representing uncertain beliefs.

Unfortunately, unadorned fuzzy sets are too limited to capture complex or potentially inconsistent beliefs, because all too often they reduce to absurdities ("nothing is possible") or trivialities ("everything is possible").

However, we show that by combining the syntax of propositional logic with the semantics of fuzzy sets a rich framework for expressing and manipulating uncertain beliefs can be created, admitting Gärdenfors-style expansion, revision, and contraction operators and being moreover amenable to easy integration with conventional ``crisp'' information processing.

The model presented here addresses many of the shortcomings of traditional approaches for building fuzzy data models, which will hopefully lead to a wider adoptance of fuzzy technologies for the creation of information systems.

Keywords

fuzzy belief revision, fuzzy information systems, soft computing, fuzzy object-oriented data model

Fuzzy Set Theory-Based Belief Processing for Natural Language Texts

Introduction

The growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of our artificial believer system we present in this paper is to extract and analyze opinionated statements from newspaper articles.

Beliefs are modeled with a fuzzy-theoretic approach applied after NLP-based information extraction. A fuzzy believer models a human agent, deciding what statements to believe or reject based on different, configurable strategies.

Attributions

Abstract

We present here the outline of an ongoing research effort to recognize, represent, and interpret attributive constructions such as reported speech in newspaper articles. The role of reported speech is attribution: the statement does not assert some information as `true' but attributes it to some source. The description of the source and the choice of the reporting verb can express the reporter's level of confidence in the attributed material.

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