LTSG:Latent Topical Skip-Gram for Mutually Improving Topic Model and Vector Representations
Topic models have been widely used in discovering latent topics which are shared across documents in text mining.Vector representations,word embeddings and topic embeddings,map words and topics into a low-dimensional and dense real-value vector space,which have obtained high performance in NLP tasks.However,most of the existing models assume the results trained by one of them are perfect correct and used as prior knowledge for improving the other model.Some other models use the information trained from external large corpus to help improving smaller corpus.In this paper,we aim to build such an algorithm framework that makes topic models and vector representations mutually improve each other within the same corpus.An EM-style algorithm framework is employed to iteratively optimize both topic model and vector representations.Experimental results show that our model outperforms state-of-the-art methods on various NLP tasks.
Topic modeling Polysemous-word Word embeddings Text mining
Jarvan Law Hankz Hankui Zhuo JunHua He Erhu Rong
Department of Computer Science,Sun Yat-Sen University,GuangZhou 510006,China
国际会议
广州
英文
375-387
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）