Tag generation method based on topic information
Yuecheng Yu, Daoyue Jing, Chang Liu, Yongqian Lu, Jinlong Shi · 2021
The traditional tag generation method of text resources is only based on the information of the text itself. However, it ignores words with low frequency but high topic relevance, resulting in low accuracy of tag generation. So, this paper bases on the traditional TextRank model, using the document-topic distribution and the distribution of words under the corresponding topics to measure the importance of words in the document, to adjust the random jump probability of nodes. Then, the similarity between word vectors and statistical feature information are used to update the weight of word nodes iteratively. As a result, a new word graph model is constructed to generate text tags. Compared with the traditional TF-IDF, TextRank and other related algorithms, the experimental results of our model on real datasets demonstrate the effectiveness of our proposed method, which has a certain improvement in accuracy, recall and F value.