Syntax dependency relationship-based named entity identification method

Abstract

The invention relates to a named entity identification method based on syntax dependency. In named entity identification, only when entity boundaries and types identified by a model are matched with boundaries and types of labeled entities, the entity is calculated to be a true positive example (TP). In most test samples, false positive examples (FP) and false negative examples (FN) are often caused by incorrect boundary judgment of entities, that is, boundary identification is much more difficult than type identification. According to the method, a self-attention mechanism is used for weakening the relation between entities and words outside the entities, and the relation between the words inside the entities is enhanced. Specifically, a self-attention mechanism is added after a bi-directional long-short term memory (Bi-LSTM) network, dependency relationships among words in a syntactic dependency tree are encoded into context information, and finally entity boundaries are judged jointly according to global features provided by the Bi-LSTM network and local features provided by the syntactic dependency tree. According to the method, the accuracy of named entity identification is improved.

Description

Technical field: [0001] The invention relates to the field of deep learning, and relates to a named entity recognition technology in text. Background technique [0002] Traditional named entity recognition methods rely on a large number of human-defined features. However, this method of manually defining features is not only time-consuming and labor-intensive, but also requires professionals with domain and language knowledge. In recent years, relying on its powerful data mining capabilities, deep learning has minimized the cost of manually constructing features, and has made remarkable achievements in the fields of image classification, speech recognition, and natural language processing. Therefore, using deep learning methods for named entity recognition has great research significance. [0003] In text, accurate identification of named entity types and their entity boundaries has a great impact on the development of complex natural language systems, such as information e...

AI Technical Summary

Problems solved by technology

In most test samples, false positives (FP) and false negatives (FN) are often caused by incorrect boundary judgments of entities, which means boundary recognition is much more difficult than type recognition

However, most deep network models do not have specific functions for boundary recognition, which makes the model often have a high accuracy rate in type judgment, but a low accuracy rate in boundary judgment.

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