71 citations · 273 across the 28 of their papers we have counts for
11 papers · 1 filter
Weakly-Supervised Hierarchical Text Classification
Yu Meng, Jiaming Shen, Chao Zhang +1
Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models a…
TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering
Chao Zhang, Fangbo Tao, Xiusi Chen +5
Taxonomy construction is not only a fundamental task for semantic analysis of text corpora, but also an important step for applications such as information filtering, recommendatio…
Mining Entity Synonyms with Efficient Neural Set Generation
Jiaming Shen, Ruiliang Lyu, Xiang Ren +3
Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on…
User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription
Yu Shi, Xinwei He, Naijing Zhang +2
Heterogeneous information networks (HINs) with rich semantics are ubiquitous in real-world applications. For a given HIN, many reasonable clustering results with distinct semantic…
Weakly-Supervised Neural Text Classification
Yu Meng, Jiaming Shen, Chao Zhang +1
Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering.…
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Yu Shi, Qi Zhu, Fang Guo +2
Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from netw…