Method for relationship classification with LSTM and CNN joint model based on location

Abstract

The invention relates to a method for relationship classification with an LSTM and CNN joint model based on location. The method includes the steps of (1) preprocessing data; (2) training word vectors; (3) extracting location vectors; acquiring the location vector feature and high-dimensional location feature vector of each word in a training set, cascading the word vector of each word with the high-dimensional location feature vector thereof to obtain a joint feature; (4) building a model for a specific task; encoding contextual information and semantic information of entities by use of bidirectional LSTM; outputting the vector of the location corresponding to the marked entities, inputting the output to CNN, outputting two entity nouns and their contextual information and relational wordinformation, and inputting the entity nouns and their contextual information and relational word information into a classifier for classification; (5) training the model by use of a loss function. The method does not need to manually extract any features, the joint model does not need to use additional natural language processing tools to preprocess the data, the algorithm is simple and clear, and the best effect at present is achieved.

Description

technical field [0001] The invention relates to a method for classifying relationships based on a position-based LSTM and CNN joint model, and belongs to the technical field of natural language processing. Background technique [0002] With the advent of the intelligent age, the processing method of big data is developing in the direction of automation and intelligence, and various jobs are gradually replaced by intelligent machines. There are more and more intersections between human society and intelligent machines. In such an era background Nowadays, intelligent and convenient human-computer interaction becomes more and more important. Therefore, the automatic construction technology of question answering system and knowledge base has received great attention and achieved some results in both industry and academia. These achievements are inseparable from the support of basic theories such as natural language processing, among which relation extraction plays an important ...

AI Technical Summary

Problems solved by technology

However, inputting the state matrix into the convolutional neural network (CNN) in this patent is likely to cause information redundancy and introduce interference information.

Images