Context Aware Textual Entailment

In conversations, stories, news reporting, and other forms of natural language, understanding requires participants to make assumptions (hypothesis) based on background knowledge, a process called entailment. These assumptions may then be supported, contradicted, or refined as a conversation or story progresses and additional facts become known and context changes. It is often the case that we do not know an aspect of the story with certainty but rather believe it to be the case; i.e., what we know is associated with uncertainty or ambiguity. In this research a method has been developed to identify different contexts of the input raw text along with specific features of the contexts such as time, location, and objects. The method includes a two-phase SVM classifier along with a voting mechanism in the second phase to identify the contexts. Rule-based algorithms were utilized to extract the context elements. This research also develops a new context˗aware text representation. This representation maintains semantic aspects of sentences, as well as textual contexts and context elements. The method can offer both graph representation and First-Order-Logic representation of the text. This research also extracts a First-Order Logic (FOL) and XML representation of a text or series of texts. The method includes entailment using background knowledge from sources (VerbOcean and WordNet), with resolution of conflicts between extracted clauses, and handling the role of context in resolving uncertain truth

Apports de la logique mathématique en ingénierie des exigences

This thesis deals with requirements engineering (RE). RE characterizes the process leading to consistent set of specifications about some product. We have identified three distinct phases in RE process: requirements modelling, inconsistency management and requirements distribution. In the modelling phase, we have used CO, a logic of preferences, which has allowed us to express each agent's requirements in an ordonned way, but also domain constraints and complex normative sentences. We have then defined the notion of consistency between those three notions. Concerning the possible conflicts between requirements emitted by different agents, we have developped MF, a modal logic allowing to reason on belief bases obtained by majority merging. We have also developped Prolog automatic prover for MF. We have then shown that our approach allows to reason on ordonned or unordonned requirements sets. Finally, we have proposed to include in the RE process a distribution phase. The requirements are distributed among a set of executive agents. We have defined a simple model of agency from which we can derive the agent's goals. We have then extended this approach to multiagents systems and defined a distribution model based on a central entity controlling the distribution process

The Semantics of Propositional Contexts

. In this paper we investigate the semantic properties of contexts. We describe the syntax and semantics of the propositional logic of context. This logic extends classical propositional logic in two ways. Firstly, a new modality, ist(; OE), is introduced. It is used to express that the sentence, OE, holds in the context . Secondly, each context has its own vocabulary, i.e. a set of propositional atoms which are defined or meaningful in that context. The main results of this paper are a proof that our logic is decidable and comparison of our semantics to Kripke semantics. 1 Introduction In this paper we investigate the semantic properties of contexts as they appear in declarative AI. Contexts were first suggested in McCarthy's Turing Award Paper, [5], as a possible solution to the problem of generality in AI. Our main motivation for formalizing contexts is to solve this problem. We want to be able to make AI systems which are never permanently stuck with the concepts they use at a gi..