Publications (17)
Hierarchical Neural Network for Extracting Knowledgeable Snippets and Documents
Ganbin Zhou, Rongyu Cao, Xiang Ao +4
In this study, we focus on extracting knowledgeable snippets and annotating knowledgeable documents from Web corpus, consisting of the documents from social media and We-media. Inf…
Reinforcement Learning to Rank Using Coarse-grained Rewards
Yiteng Tu, Zhichao Xu, Tao Yang +6
Learning to rank (LTR) plays a crucial role in various Information Retrieval (IR) tasks. Although supervised LTR methods based on fine-grained relevance labels (e.g., document-leve…
Neural Snowball for Few-Shot Relation Learning
Tianyu Gao, Xu Han, Ruobing Xie +4
Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient trai…
Harnessing Multimodal Large Language Models for Personalized Product Search with Query-aware Refinement
Beibei Zhang, Yanan Lu, Ruobing Xie +4
Personalized product search (PPS) aims to retrieve products relevant to the given query considering user preferences within their purchase histories. Since large language models (L…
Universality of preference behaviors in online music-listener bipartite networks: A Big Data analysis
Xiao-Pu Han, Fen Lin, Jonathan J. H. Zhu +1
We investigate the formation of musical preferences of millions of users of the NetEase Cloud Music (NCM), one of the largest online music platforms in China. We combine the method…
Language Modeling with Sparse Product of Sememe Experts
Yihong Gu, Jun Yan, Hao Zhu +5
Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words. In this paper, we argue that words are atomic language units but no…