59 citations · 113 across the 5 of their papers we have counts for
9 papers
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations
Yu Meng, Yunyi Zhang, Jiaxin Huang +2
Topic models have been the prominent tools for automatic topic discovery from text corpora. Despite their effectiveness, topic models suffer from several limitations including the…
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
Yu Meng, Yunyi Zhang, Jiaxin Huang +4
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw…
Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
Yu Meng, Yunyi Zhang, Jiaxin Huang +3
Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora. To account for potential hier…
Partially-Typed NER Datasets Integration: Connecting Practice to Theory
Shi Zhi, Liyuan Liu, Yu Zhang +4
While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them. Instead o…
Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
Carl Yang, Yuxin Xiao, Yu Zhang +2
Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic super…
Discriminative Topic Mining via Category-Name Guided Text Embedding
Yu Meng, Jiaxin Huang, Guangyuan Wang +4
Mining a set of meaningful and distinctive topics automatically from massive text corpora has broad applications. Existing topic models, however, typically work in a purely unsuper…