56 citations · 203 across the 18 of their papers we have counts for
8 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…
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering
Jiaming Shen, Maryam Karimzadehgan, Michael Bendersky +2
User information needs vary significantly across different tasks, and therefore their queries will also differ considerably in their expressiveness and semantics. Many studies have…
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.…
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
Yuning Mao, Xiang Ren, Jiaming Shen +2
We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e.,…