Automated Tool Design Of Subjective Question Answering Using Text Mining Techniques

Abstract

The automatic acquisition of semantics in natural language texts has always been a challenging task as these texts bear a relatively high degree of word-sense ambiguity, preferably in English. However, this has always been a keen arena of research by the Natural Language Processing (NLP) experts, particularly in designing the automated Question-Answering Evaluation Systems. Recent research trends in Subjective Question Answering assessment task are intended for defining the program structures capable of addressing the question processing and answer extraction subtasks and combine them in increasingly sophisticated ways. The functionality of Digital Libraries currently fails to spot the generic methods for the smooth integration of content stemming from different sources and a flexible framework for implementing new functionalities. The earlier developments showed the only way to assess the answers involving the manual determination of whether an information nugget appears in a candidate s response or not. To some extent, when the evaluations are performed on large scale, say in academia, it has been observed that human evaluations remain biased and are subject to specific guidelines given to human assessors. newlineThe overall system s functionality relies on three main components, namely, question processing component, the search component and the answer-evaluation component. The parts of e-book in the knowledge sources are annotated and classified according to the bootstrapping ontologies of the subject domain. In continuum with the efforts put by computational linguistic communities towards automatic retrieval of text semantics, an algorithm is designed to automatically generate model answer for any keyed-in question or series of related questionnaires. An attempt is made to extract the most meaningful textual fragments from natural language sentences, highlighting the semantic sense of the explained discourse. The research focus roots upon extracting the noun phrases existing in dominating subject and object roles