NLP-Based Semantic and Phraseo-Semantic Fields for Data Mining Operations

It tests and challenges the assumption, prevalent in computational linguistics thinking and in the semantic field framework, that the delivery of NLP-based phraseo-semantic modeling is associated with better data mining accuracy. Since the opening of large-scale text analytics, the application of semantic networks in data mining operations has developed rapidly, the overall algorithmic strength has been increasing, and the evaluation standards have been continuously improved. Therefore, on the basis of studying the related theories of SEM algorithm and TOPSIS algorithm, this paper uses the advantages of the two algorithms, combines the two algorithms, and proposes an improved algorithm for context–adaptive semantic weighting algorithm. This study aims to fill this gap by examining the impact of semantic field structuring on the efficiency of phraseo-semantic extraction from the perspective of the data mining process. A new multilingual corpus data set is constructed covering 2018–2025, allowing the actual relationship between extraction performance and semantic coherence and quality to be estimated using summary statistics–driven regression models which control for both general changes over time and the time- invariant differences between data sources. According to the characteristics of the phraseo- semantic network, an improved hybrid SEM–TOPSIS algorithm is designed to solve the problem, and the effectiveness of the combined adaptive algorithm is verified through an example. Semantic density and lexical dispersion appear to be important determinants of the robustness of NLP-based operations, whereas there are substantial differences in the influencing mechanism of contextual variance and structural ambiguity on the accuracy of data mining results.

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