Multi-source and multi-label text classification method and system based on improved seq2seq model

Abstract

The invention belongs to the technical field of natural language processing text classification, in particular to a multi-source multi-label text classification method based on an improved seq2seq model and a system thereof. The method comprises the following steps: data input and pretreatment, word embedding, encoding, encoding and splicing, decoding, model optimization and prediction output. Themethod of the invention has the following beneficial effects: adopting a seq2seq depth learning framework, constructing a plurality of encoders, and combining the attention mechanism to be used for atext classification task, so as to maximize the use of multi-source corpus information and improve the classification accuracy of the multi-label; In the error feedback process of decoding step, according to the characteristics of multi-label text, an intervention mechanism is added to avoid the influence of label sorting, which is more in line with the essence of multi-label classification problem. The encoder adopts the circulating neural network, which can learn according to the time step effectively. The decoding layer adopts one-way loop neural network and adds attention mechanism to highlight the learning focus.

Description

technical field [0001] The invention belongs to the technical field of natural language processing text classification, and in particular relates to a multi-source multi-label text classification method and system based on an improved seq2seq model. Background technique [0002] Automatic text classification is one of the main tasks of natural language processing technology. Multi-label text classification is used to deal with the situation that the category of text corresponds to more than one label. The problem of multi-label text classification is very common in real life. The description text of , may correspond to multiple police categories: "taking drugs", "intentionally hurting people", etc. However, compared with single-label text classification, research on multi-label text classification is less, and its performance generally cannot reach the level of single-label text classification. [0003] In addition, the description of a thing may correspond to multiple text...

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Problems solved by technology

[0005] The present invention proposes a multi-source multi-label text classification method and its system based on the improved seq2seq model. The main improvements to the traditional seq2seq model are reflected in the addition of multiple encoders and the definition of a loss function that is not sensitive to the label order, which can effectively solve the problem of Text classification problems where the input is multi-source text data and the output is multi-label

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