2017/01/10 by Yang Xu, Jiawei Liu, Xu, Yang +1 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1701.02481
openalex publication_date 2017/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose three novel models to enhance word embedding by implicitly using morphological information. Experiments on word similarity and syntactic analogy show that the implicit models are superior to traditional explicit ones. Our models outperform all state-of-the-art baselines and significantly improve the performance on both tasks. Moreover, our performance on the smallest corpus is similar to the performance of CBOW on the corpus which is five times the size of ours. Parameter analysis indicates that the implicit models can supplement semantic information during the word embedding training process.