Deep Learning on Improved Word Embedding Model for Topic Classification
盈盈 周 · Computer Science and Application · 2016
Topic classification has wide applications in content searching and information filtering.It can be divided into two core parts: text embedding and classification modeling.In recent years, methods have brought out significant results using distributed word embedding as input and convolutional neural network (CNN) as classifiers.This paper discusses the impact of different word embedding for CNN classifiers, proposes topic2vec, a new word embedding specifically suitable for Chinese corpora, and conducts an experiment on Zhihu, a representative content-oriented internet community.The experiment turns out that CNN with topic2vec gains an accuracy of 98.06% for long content texts, 93.27% for short title texts and an improvement comparing with other word embedding models.