Improving the Relation Classification Using Convolutional Neural Network

Abstract

Relation extraction has been the emerging research topic in the field of Natural Language Processing. The proposed work classifies the relations among the data considering the semantic relevance of words using word2vec embeddings towards training the convolutional neural network. We intended to use the semantic relevance of the words in the document to enrich the learning of the embeddings for improved classification. We designed a framework to automatically extract the relations between the entities using deep learning techniques. The framework includes pre-processing, extracting the feature vectors using word2vec embedding, and classification using convolutional neural networks. We perform extensive experimentation using benchmark datasets and show improved classification accuracy in comparison with the state-of-the-art methodologies using appropriate methods and also including the additional relations.

An English Flipped Classroom Teaching Model Based on Big Data Analysis

“…The evaluation text represented by the word vector is input into the convolutional neural network, and the convolutional neural network outputs the corresponding label [ 20 , 21 ]. Annotate the part of speech of the evaluation text, extract the nouns in the evaluation text, and obtain the feature word set.…”