论文标题
代表文本数据的声明性内存结构
Declarative Memory-based Structure for the Representation of Text Data
论文作者
论文摘要
在智能计算的时代，文本处理中的计算进步是必不可少的考虑因素。已经开发了许多系统来处理不同语言的文本。但是，这有很大的发展，但他们仍然缺乏对文本的理解，即，许多人将文本视为数据，而不是将文本视为数据。在这项工作中，我们介绍了一个受人体记忆基础结构影响的文本表示方案。由于文本本质上是声明性的，因此结构组织将促进有效的计算。我们利用长期的情节记忆来保持文本信息随着时间的推移而观察到。这不仅以有组织的方式保持文本片段，而且还可以减少冗余并存储它们之间的时间关系。 WordNet已被用来模仿语义记忆，该记忆在单词层面上起作用，以促进对文本中各个单词的理解。报告了随着时间的流逝，对情节记忆进行的各种操作的实验结果。
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.
