Multi-label text classification processing method and system and information data processing terminal

Abstract

The invention belongs to the technical field of natural language processing, and discloses multi-label text classification processing method and system and an information data processing terminal. Themethod comprises the steps of: obtaining a data set; preprocessing the data set and dividing the data set into a training set and a test set; finely adjusting and extracting global feature vectors ofwords in the text sequence through a BERT pre-training model, and aggregating the global feature vectors by adopting a convolutional neural network to obtain semantic vectors of the words in the textsequence; constructing an attention weight coefficient matrix, and respectively weighting the semantic vector of each word and a weight coefficient vector in the optimal weight coefficient matrix toobtain an attention vector of the label; and normalizing the attention vectors of the tags to obtain the probability of each tag, and selecting several tags with the maximum probability as the category of the text. According to the method, global and local features of the text sequence are extracted, the influence of keywords in the text on tag categories is considered, and the classification accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a multi-label text classification processing method and system, and an information data processing terminal. Background technique [0002] Text classification is the process of classifying texts into a certain category or categories according to a certain classification system or standard, which is of great significance in the fields of natural language processing and text mining. At present, text classification has been widely used in many fields such as information retrieval, automatic classification of Web documents, automatic summarization, and text filtering. Multi-label text classification, which is different from traditional binary classification or multi-classification, deals with the task of multiple categories of text in real life, which is a complex and challenging task in natural language processing. [0003] At present, multi-label te...

AI Technical Summary

Problems solved by technology

The existing methods have the following technical problems: (1) When extracting text semantic information, when expressing the semantics of words in a text sequence, the length of the contextual text cannot be flexibly adjusted according to the length of the sentence. Even if the context is considered in the text, word order is rarely considered ; (2) During model training, the correlation between tags and some keywords in the text sequence often plays a very important role, and most existing models ignore this relationship; (3) When predicting tags, it is usually assumed that all tags Both are related to the text, making the text irrelevant or less relevant labels, and the probability is also calculated, which increases the training error and reduces the model performance and classification accuracy

[0004] The difficulty of solving the above problems and defects is: on the one hand, it lies in the more accurate expression of the semantic information of each word in the text from the text information; Neglect, when classifying, the category cannot be judged based on the most important word in the sentence

Images