An Efficient Approach to Construct Object Model of Static Textual Structure with Dynamic Behavior Based on Q-learning

Developing information technology led to raise the intricacy of information systems intensive, hence techniques with effectiveness and efficiency are required. These techniques are used to support users in using the information for rapid and correct decision-making. Conventional text mining and managing systems mainly use the presence or absence of keywords to discover and analyze useful information from textual documents. However, simple word counting and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the ability to mine the texts. This paper has been primarily concerned with constructing text representation model and exploiting that in mining and managing operations such as gathering, searching, filtering, retrieving, extracting, clustering, classifying, and summarizing. This representation model is based on semantic notions to represent text in documents, to infer unknown dependencies and relationships among concepts in a text, to measure the relatedness between text documents, and to apply mining processes using the representation and the relatedness measure. This model reflects the existing relations among concepts and facilitates accurate relatedness measurements that result in better mining performance. The experimental evaluations were carried out on real datasets from various domains, showing the importance of the proposed model.

並列關鍵字

object oriented text analysis ； reinforcement learning ； Q-learning ； NLP ； semantic relationship ； Xml technology ； and graph structure

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