TL;DR: In this article, a natural language (NL) analyzing system is provided with the capability to analyze NL expressions and to resolve ambiguities and present them to the user for verification of correct interpretation.
Abstract: A natural language (NL) analyzing system is provided with the capability to analyze NL expressions and to resolve ambiguities and present them to the user for verification of correct interpretation. A conceptual model of the system, relevant to the application in which the invention is implemented, is created (customizing the system) by the user, and is stored as a conceptual schema. The schema is built of logical facts representing entities (concepts) and relationships between entities, forming a description of the universe of discourse or object system in question. The entities of the schema have at least one external connection, namely to natural language terms in a vocabulary. The schema itself is completely language independent, though the components of it may have "names" expressed in a natural language such as English. There may be a second connection to the entities, namely where the system is used in a query system for relational data bases. In this case the entities of the schema represent objects in the data base, and thus there is a connection between the entities and those objects of the data base. The actual analysis of NL expressions is performed by a natural language engine (NLE) in cooperation with an analysis grammar and the schema. The analysis results in an intermediate, language-independent logic form representation of the input, which is paraphrased back to NL for verification. If the input is a query, there is a translation into a query language such as SQL.
TL;DR: A quantitative framework for the integration of logic and heuristic knowledge that is expressible in prepositional logic form in MINLP optimization models for process synthesis is proposed, as well as a systematic method for adjusting weights for violation of heuristics.
TL;DR: This paper reports on LCC's participation at the Third PASCAL Recognizing Textual Entailment Challenge and highlights this year's innovations which contributed to an overall accuracy of 72.25% for the RTE 3 test data.
Abstract: This paper reports on LCC's participation at the Third PASCAL Recognizing Textual Entailment Challenge. First, we summarize our semantic logical-based approach which proved successful in the previous two challenges. Then we highlight this year's innovations which contributed to an overall accuracy of 72.25% for the RTE 3 test data. The novelties include new resources, such as eXtended WordNet KB which provides a large number of world knowledge axioms, event and temporal information provided by the TARSQI toolkit, logic form representations of events, negation, coreference and context, and new improvements of lexical chain axiom generation. Finally, the system's performance and error analysis are discussed.
TL;DR: In this paper, a multi-modal natural language question answering system and method comprises receiving a question logic form, at least one answer logic form and utilizing semantic relations, contextual information, and adaptable logic.
Abstract: A multi-modal natural language question answering system and method comprises receiving a question logic form, at least one answer logic form, and utilizing semantic relations, contextual information, and adaptable logic.