Information Extraction with Semantic Term Relation Learning combined with Document Classification using Ranking and Trust Assessment

Abstract

In the era of abundant information, getting efficient and correct information from unstructured documents is of highest importance. This paper proposes a detailed method for information extraction, which includes learning of relationships between terms using semantics and classifying documents with ranks and scores of trust. The method uses semantic analysis to understand the relationships between terms in the documents and using natural language processing methods, the model identifies and extracts relationships between terms. The relationship between the terms act as indications for understanding the context and retrieving the information from the documents. The agent uses neuro-fuzzy rules in order to deal with time-based data; a deep neural network that is based on semantic similarity then sorts this data by semantic relationships. The system looks at document features and the semantic connections between terms for classification. A ranking system scores each document by relevance and trustworthiness to increase usability. The ranking is based on things like the source's reliability, past accuracy, and user input. This makes sure that users see the most relevant and reliable documents, which improves their experience and trust in the system. This method helps increase the correctness and reliability of information extraction systems, allowing users to access useful and trustworthy information from many unstructured documents.