no code implementations • 26 Mar 2024 • Yiqun Chen, Jiaxin Mao, Yi Zhang, Dehong Ma, Long Xia, Jun Fan, Daiting Shi, Zhicong Cheng, Simiu Gu, Dawei Yin
The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible.
no code implementations • 11 Nov 2022 • Lianshang Cai, Linhao Zhang, Dehong Ma, Jun Fan, Daiting Shi, Yi Wu, Zhicong Cheng, Simiu Gu, Dawei Yin
In this paper, we focus on two key questions in knowledge distillation for ranking models: 1) how to ensemble knowledge from multi-teacher; 2) how to utilize the label information of data in the distillation process.
no code implementations • 24 May 2021 • Lixin Zou, Shengqiang Zhang, Hengyi Cai, Dehong Ma, Suqi Cheng, Daiting Shi, Zhifan Zhu, Weiyue Su, Shuaiqiang Wang, Zhicong Cheng, Dawei Yin
However, it is nontrivial to directly apply these PLM-based rankers to the large-scale web search system due to the following challenging issues:(1) the prohibitively expensive computations of massive neural PLMs, especially for long texts in the web-document, prohibit their deployments in an online ranking system that demands extremely low latency;(2) the discrepancy between existing ranking-agnostic pre-training objectives and the ad-hoc retrieval scenarios that demand comprehensive relevance modeling is another main barrier for improving the online ranking system;(3) a real-world search engine typically involves a committee of ranking components, and thus the compatibility of the individually fine-tuned ranking model is critical for a cooperative ranking system.
2 code implementations • IJCNLP 2019 • Lianzhe Huang, Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang
Recently, researches have explored the graph neural network (GNN) techniques on text classification, since GNN does well in handling complex structures and preserving global information.
Ranked #2 on Text Classification on Ohsumed
no code implementations • ACL 2019 • Dehong Ma, Sujian Li, Fangzhao Wu, Xing Xie, Houfeng Wang
Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem.
Ranked #1 on Term Extraction on SemEval 2014 Task 4 Laptop
no code implementations • EMNLP 2018 • Dehong Ma, Sujian Li, Houfeng Wang
Targeted sentiment analysis (TSA) aims at extracting targets and classifying their sentiment classes.
no code implementations • IJCNLP 2017 • Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang, Xu sun
Document-level sentiment classification aims to assign the user reviews a sentiment polarity.
Ranked #5 on Sentiment Analysis on User and product information
5 code implementations • 4 Sep 2017 • Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang
In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning.