Relationship extraction method combining neural network and feature calculation

Abstract

The invention discloses a relationship extraction method combining a neural network and feature calculation. The method comprises the following steps of 1, performing vector mapping on a text based ona random word vector; 2, extracting atomic features in the sentence, performing feature calculation on the atomic features to obtain composite features, and performing vector mapping on the compositefeatures; 3, performing convolution pooling operation on the word vector matrix through a neural network to extract features; 4, splicing a result after convolution pooling with the composite featurevector in the sentence; and step 5, performing full connection, and predicting a result on a Softmax layer. On the basis of making full use of sentence text complete information, structure and semantic information is obtained in combination with a feature calculation method. Simultaneous introduction of neural network technology, according to the method, the characteristic that a neural network automatically extracts high-dimensional abstract features in a layered mode is brought into full play, a result obtained after sentence word vectors are input into a convolution pooling layer is combined with composite feature vectors, the problem of feature sparseness caused by the limited number of words in sentences is avoided to a certain extent, and therefore the experimental performance of arelation extraction task is effectively improved.

Description

technical field [0001] The invention relates to a convolutional neural network, in particular to a relation extraction method combining neural network and feature calculus, and belongs to the technical field of natural language processing. Background technique [0002] With the rapid development of computer technology, the amount of data in the network is also increasing exponentially. How to quickly and accurately analyze the information needed by users from these data has become a problem that people are increasingly concerned about. Content of the study. Information extraction is to extract specific information from structured, semi-structured or unstructured text and store it in a structured database. Relation extraction is a subtask of information extraction and an important research topic in the field of information extraction. [0003] As a subtask in the research field of information extraction, relation extraction has been highly valued by researchers and many stu...

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Problems solved by technology

Its advantage is high accuracy, and its disadvantage is that it needs manual construction, depends on specific fields, and is difficult to expand and maintain

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