Chinese entity relationship extraction method based on character and word feature fusion of entity meaning items

Abstract

The invention relates to a Chinese entity relationship extraction method based on character and word feature fusion of entity meaning items. The method comprises the following steps of introducing entity meaning items to expand sentences into triples (sentences, entity 1 meaning items and entity 2 meaning items), enriching input fine grit and mapping three sequences in the triples into word vectormatrixes respectively; inputting statements in the triples into the two models in parallel, wherein one model is a two-way long and short-term memory network (Att-BLSTM) based on an attention mechanism to learn character features and the other model is one to learn partial features through a convolutional neural network (CNN) and learn word features through Att-BLSTM; respectively using Att-BLSTMto learn character-based entity 1 semantic item features and word-based entity 2 semantic item features and fusing four features into one feature that can fully characterize semantic information, which is used for relation extraction.According to the method, word segmentation errors can be avoided, the problem that one word has multiple meanings is solved, the Chinese entity relationship extraction accuracy is effectively improved, and the method can be widely applied to knowledge graph construction.

Description

technical field [0001] The invention belongs to the technical field, and relates to a Chinese entity relationship extraction method based on the fusion of words and word features of entity meaning items. Background technique [0002] With the development of network technology, the information age based on text and images is coming, and it is particularly important to obtain useful information from a large amount of unstructured text data. The main purpose of entity relationship extraction is to determine the relationship category between entity pairs in unstructured text on the basis of entity recognition, and to form structured data for storage and retrieval. For example, for a sample "[Youlan] e1 in [valley] e2 , Originally no one knows. ", with two marked entities "Youlan" and "Valley". The task of relation extraction is to obtain the semantic information of the sample through machine learning, to complete the identification of the relationship between entity pairs, an...

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Problems solved by technology

In other words, as the language environment changes, none of the above extraction methods can handle word polysemy

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