CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 USC 119(a) of Korean Patent Applications No. 10-2020-0040780 filed on Apr. 3, 2020, and No. 10-2019-0056169 filed on May 14, 2019, in the Korean Intellectual Property Office, the entire disclosures of which are incorporated herein by reference for all purposes.

BACKGROUND

1. Field

The present disclosure relates to a method for classifying a given text into categories using a multi-task learning framework, and more particularly, to a method for simultaneously learning text classification tasks of different scales by utilizing a deep learning-based multi-task learning framework, and classifying texts therethrough.

2. Description of the Related Art

As a deep learning technique brings great advances in a computer vision system, research on a natural language processing system using the deep learning is also rapidly progressing. In particular, a text classification technique, which is a core in the natural language processing system, achieves a great performance improvement through the deep learning technique. However, in a large-scale text classification task having thousands and tens of thousands of categories to be classified, the deep learning technique is showing poor performance due to a lack of learning documents per category and an imbalance in an amount of learning data between categories.

For the large-scale text classification, categories of the large-scale classification system have been expressed by using an explicit expression model. This method shows stable performance while sharing learning data by using a tree structure of categories, but has a problem that the performance is dependent on an associated knowledge base. In addition, the explicit expression model has a disadvantage that it may not calculate semantic similarity between different words. On the other hand, an implicit expression model capable of calculating the semantic similarity between words and sentences such as deep learning and embedding has utilized the multi-task learning to share the learning data. However, the technique for the deep learning-based multi-task learning also showed great performance improvement in the small-scale text classification task that classifies the text into at least 2 to dozens of categories, but the large-scale text classification tasks of thousands and tens of thousands were unable to share data effectively. To overcome this limitation, if there is a learning method for the deep learning that may share data even in the large-scale classification system, the deep learning technique showing strong performance may be used for the large-scale text classification task.

TECHNICAL LITERATURE

Patent Literature

Korean Patent Registration No. 10-1860472 (Title of the Invention: TEXT CLASSIFIER BASED ON OPEN DIRECTORY PROJECT, APPARATUS AND METHOD OF GENERATION AND CLASSIFICATION FOR TEXT CLASSIFIER)

SUMMARY

Provided are to solve the above-mentioned problems, and a method for effectively classifying a text by calculating semantic similarity between words.

In addition, an object of the disclosure is to solve the problem of lack of learning data by sharing learning data in text classification tasks having different scales.

However, a technical problem to be solved by the present example is not limited to the technical problem as described above, and other technical problems may exist.

Additional aspects will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the presented embodiments.

According to an aspect of an embodiment, a method for a text classification using multi-task learning executed by a server, the method includes: a step of generating text classification learning data by executing a pre-processing process on raw data collected through a network; a step of learning a category classification in a first classification system preset through a first text classification model using the text classification learning data as an input; a step of learning a category classification in a second classification system preset through a second text classification model using the text classification learning data as an input; and a step of classifying the category in the second classification system by inputting text data received from a user terminal into the second text classification model. The first text classification model and the second text classification model are learned by sharing a feature used for the category classification generated from a shared layer through a multi-task learning framework based on an artificial neural network. The first classification system and the second classification system are generated by using category data based on an open directory project (ODP).

According to an aspect of another embodiment, a text classification server using multi-task learning, the server includes: a memory in which a text classification program using multi-task learning is stored; and a processor executing the program stored in the memory. The processor generates text classification learning data by executing a pre-processing process on raw data being collected through a network by execution of the program, learns the category classification in a first classification system preset through a first text classification model by using the text classification learning data as an input, learns the category classification in a second classification system preset through a second text classification model by using the text classification learning data as an input, and classifies the category in the second classification system by inputting text data received from a user terminal into the second text classification model. The first text classification model and the second text classification model are learned by sharing a feature used for the category classification generated from a shared layer through a multi-task learning framework based on an artificial neural network. The first classification system and the second classification system are generated by using category data based on an open directory project (ODP).

According to any one of the problem solving means of the present application described above, it is possible to obtain a learning data enhancement effect in a large-scale text classification where there is lack of learning documents.

Since the present disclosure applies a method of multi-task learning to a convolution neural network suitable for the large-scale text classification and solves the problems of the present disclosure described above, it shows a higher classification accuracy with less learning and classification times than those of a Recurrent neural network. Therefore, there is an advantage of being able to take both efficiency and effectiveness in the large-scale text classification.

