  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.

Artificial Intelligence;Computation and Language