An intelligent question: answering system for natural language

As applications of information storage and retrieval systems are becoming more widespread, there is an increased need to be able to communicate with these systems in a natural way. Natural Language applications in the 1990s, as well as in the foreseeable future, have more demanding requirements. Current Natural Language Processing approaches alone have proven to be insufficient as they lack to obtain linguistic understanding. A more suitable approach would be to adopt Computational Linguistics theories, such as the Lexical-Functional Grammar (LFG) theory complemented with Artificial Intelligence representation and processing techniques. A prototype Question-Answering System has been developed. It takes Natural Language parsed interrogatives, produces the Functional and Semantic structures according to the LFG representation. It compares the functional behaviour of verbs and their linguistic associations in a given query with a general Object Model in that specific domain. It will then attempt to deduce more information from the given processed text and represent it for possible queries. The structural rules of the LFG and the deduced common-sense domain specific information resolve most of the common ambiguities found in Natural Languages and enhance the understanding ability of the proposed prototype. The LFG theory has been adopted and extended: (i) to examine the constituents of the theoretical, syntactic and semantic of Arabic interrogatives, an area which has not been thoroughly investigated, (ii) to represent the Functional and Semantic Structures of the Arabic interrogatives, (iii) to overcome the word-order problem associated with some Natural languages such as Arabic, (iv) to add understanding capabilities by capturing the common-sense domain specific knowledge within a specific domain

A vector measure for the intelligence of a question-answering (Q-A) system

The problem of quantification of intelligence of humans, and of intelligent systems, has been a challenging and controversial topic. IQ tests have been traditionally used to quantify human intelligence based on results of test designed by psychologists. It is in general very difficult to quantify intelligence. In this paper the authors consider a simple question-answering (Q-A) system and use this to quantify intelligence. The authors quantify intelligence as a vector with three components. The components consist of a measure of knowledge in asking questions, effectiveness of questions asked, and correctness of deduction. The authors formalize these parameters and have conducted experiments on humans to measure these parameter