Novel semantic tagging detection algorithms based non-negative matrix factorization

Abstract

The tagging aims to address a challenge to search relevant text-documents given a set of tags. In addition, the tag-based approaches received a wide attention as a possible solution to the big-content. Probabilistic topic model methods, such as Dirichlet distribution and non-negative matrix factorization are used for tagging process. Both have many challenges. The iterations in addition the semantic coherence are considered as challenges in semantic tagging applications. In light of this, we propose a novel learning tagging model called semantic non-negative matrix factorization, which introduces the utilization of the semantic text representation via knowledge-based approach to extract the term-topic matrix and the topic-document matrix by semantically approach. The proposed words are based on a novel initialization method for non-negative matrix factorization technique. In the experimental evaluations, we use five datasets demonstrate the effectiveness of our model. The results are compared with the state-of-the-art model. The results show the proposed model has an ability to generate more precise topics with semantically related and having the high sense to the disambiguation of meaning, provides up to more dimensionality reduction and the topic coherence based semantic.

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“…Several approaches have been proposed for the semantic tagging of texts. Gadelrab et al [41] proposed a tagging model based on a nonnegative matrix factorization technique to extract topics from texts. The approach makes use of lexical semantic correlation to capture semantics from text and performs well compared to state-of-the-art models.…”