  This research work deals with Natural Language Processing (NLP) and
extraction of essential information in an explicit form. The most common among
the information management strategies is Document Retrieval (DR) and
Information Filtering. DR systems may work as combine harvesters, which bring
back useful material from the vast fields of raw material. With large amount of
potentially useful information in hand, an Information Extraction (IE) system
can then transform the raw material by refining and reducing it to a germ of
original text. A Document Retrieval system collects the relevant documents
carrying the required information, from the repository of texts. An IE system
then transforms them into information that is more readily digested and
analyzed. It isolates relevant text fragments, extracts relevant information
from the fragments, and then arranges together the targeted information in a
coherent framework. The thesis presents a new approach for Word Sense
Disambiguation using thesaurus. The illustrative examples supports the
effectiveness of this approach for speedy and effective disambiguation. A
Document Retrieval method, based on Fuzzy Logic has been described and its
application is illustrated. A question-answering system describes the operation
of information extraction from the retrieved text documents. The process of
information extraction for answering a query is considerably simplified by
using a Structured Description Language (SDL) which is based on cardinals of
queries in the form of who, what, when, where and why. The thesis concludes
with the presentation of a novel strategy based on Dempster-Shafer theory of
evidential reasoning, for document retrieval and information extraction. This
strategy permits relaxation of many limitations, which are inherent in Bayesian
probabilistic approach.
