Text Information Data Mining Method in Natural Language Processing Tasks

Text mining methods often rely on a single data source or simple word frequency statistics, making it difficult to capture multi-source text semantic associations and local contextual dependencies, resulting in poor mining accuracy. Therefore, a method for text information data mining in natural language processing tasks is proposed. Using Python web crawlers to obtain multi-source text data, after preprocessing such as cleaning, segmentation, and removal of stop words, a Vector Space Model (VSM) is used for text representation, and a TF-IDF (Term Frequency Across Document Frequency) weight optimization mechanism is introduced to enhance feature semantic representation. On this basis, a semantic enhancement system is constructed based on the BERT classification model in the field of natural language processing. Through the self-attention mechanism of multi-layer Transformer encoders, semantics are aggregated to effectively capture local contextual dependencies, and context-sensitive word vectors are generated by the output layer. Finally, by fine-tuning the parameters of the BERT (Bidirectional Encoder Representation from Transformers) model and combining it with the Softmax function, precise mining of text information data categories was achieved. The experimental results show that in the embedding experiment of sports news headlines, this method can form a semantic aggregation structure with clear domain logic for word vectors; In the cross domain short text classification experiment, the overall accuracy of this method on the dataset reached 95.7%, which was 19.5% and 18.7% higher than the comparative methods, effectively solving the cross domain ambiguity problem in natural language processing.