1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CL2023★ 1 cited
Frequency-aware Dimension Selection for Static Word Embedding by Mixed Product Distance
Lingfeng Shen, Haiyun Jiang, Lemao Liu +1
Static word embedding is still useful, particularly for context-unavailable tasks, because in the case of no context available, pre-trained language models often perform worse than…
stat.ML2021★ 1 cited
Concentrated Document Topic Model
Hao Lei, Ying Chen
We propose a Concentrated Document Topic Model(CDTM) for unsupervised text classification, which is able to produce a concentrated and sparse document topic distribution. In partic…
stat.ML2021
Exclusive Topic Modeling
Hao Lei, Ying Chen
We propose an Exclusive Topic Modeling (ETM) for unsupervised text classification, which is able to 1) identify the field-specific keywords though less frequently appeared and 2) d…