Declarative Memory-based Structure for the Representation of Text Data
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In the era of intelligent computing, computational progress in text processing is an essential consideration. Many systems have been developed to process text over different languages. Though, there is considerable development, they still lack in understanding of the text, i.e., instead of keeping text as knowledge, many treat text as a data. In this work we introduce a text representation scheme which is influenced by human memory infrastructure. Since texts are declarative in nature, a structural organization would foster efficient computation over text. We exploit long term episodic memory to keep text information observed over time. This not only keep fragments of text in an organized fashion but also reduces redundancy and stores the temporal relation among them. Wordnet has been used to imitate semantic memory, which works at word level to facilitate the understanding about individual words within text. Experimental results of various operation performed over episodic memory and growth of knowledge infrastructure over time is reported.
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