no code implementations • Findings (ACL) 2022 • Yubo Chen, Yunqi Zhang, Yongfeng Huang
To capture the relation type inference logic of the paths, we propose to understand the unlabeled conceptual expressions by reconstructing the sentence from the relational graph (graph-to-text generation) in a self-supervised manner.
no code implementations • CCL 2020 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
In addition, we propose an auxiliary term classification task to predict the types of the matched entity names, and jointly train it with the NER model to fuse both contexts and dictionary knowledge into NER.
no code implementations • CCL 2020 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
We compute a title-body matching score based on the representations of title and body enhanced by their interactions.
no code implementations • 3 Feb 2025 • Hui Shen, JingXuan Zhang, Boning Xiong, Rui Hu, Shoufa Chen, Zhongwei Wan, Xin Wang, Yu Zhang, Zixuan Gong, Guangyin Bao, Chaofan Tao, Yongfeng Huang, Ye Yuan, Mi Zhang
Diffusion models have emerged as powerful generative models capable of producing high-quality contents such as images, videos, and audio, demonstrating their potential to revolutionize digital content creation.
1 code implementation • 6 Oct 2024 • Yijiong Yu, Ma Xiufa, Fang Jianwei, Zhi Xu, Su Guangyao, Wang Jiancheng, Yongfeng Huang, Zhixiao Qi, Wei Wang, Weifeng Liu, Ran Chen, Ji Pei
Long-context language models (LCLMs), characterized by their extensive context window, are becoming popular.
no code implementations • 9 Sep 2024 • Jiahao Lai, Jiaqi Li, Jian Xu, Yanru Wu, Boshi Tang, Siqi Chen, Yongfeng Huang, Wenbo Ding, Yang Li
In this framework, we deploy a diffusion model on the server to integrate the diverse parameter distributions and propose a parameter inversion method to efficiently generate a set of personalized parameters for each client.
no code implementations • 1 Aug 2024 • Zhen Yang, Wenhui Wang, Tao Qi, Peng Zhang, Tianyun Zhang, Ru Zhang, Jianyi Liu, Yongfeng Huang
Then candidate news representation can be formed to match user representation to achieve news recommendation.
no code implementations • 22 Jul 2024 • Chunzhen Jin, Yongfeng Huang, Yaqi Wang, Peng Cao, Osmar Zaiane
Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources.
1 code implementation • 4 Jun 2024 • Yijiong Yu, Huiqiang Jiang, Xufang Luo, Qianhui Wu, Chin-Yew Lin, Dongsheng Li, Yuqing Yang, Yongfeng Huang, Lili Qiu
This paper first explores the micro-level manifestations of position bias, concluding that attention weights are a micro-level expression of position bias.
no code implementations • 28 Apr 2024 • Minhao Bai, Kaiyi Pang, Yongfeng Huang
In the rapidly evolving domain of artificial intelligence, safeguarding the intellectual property of Large Language Models (LLMs) is increasingly crucial.
no code implementations • 18 Apr 2024 • Yongfeng Huang, Zhendong Chen, Kun Ye, Lang Zhou, Haixin Sun
In this letter, we investigate a new generalized double Pareto based on off-grid sparse Bayesian learning (GDPOGSBL) approach to improve the performance of direction of arrival (DOA) estimation in underdetermined scenarios.
no code implementations • 14 Mar 2024 • Chang Zong, Yuyan Chen, Weiming Lu, Jian Shao, Yongfeng Huang, Heng Chang, Yueting Zhuang
Large Language Models (LLMs) have demonstrated efficacy in various linguistic applications, including question answering and controlled text generation.
1 code implementation • 24 Jan 2024 • Dehao Tao, Congqi Wang, Feng Huang, JunHao Chen, Yongfeng Huang, Minghu Jiang
Most existing methods use a paradigm that treats the question as the objective, with relevant knowledge being incrementally retrieved from the knowledge graph.
no code implementations • 18 Dec 2023 • Yijiong Yu, Yongfeng Huang, Zhixiao Qi, Zhe Zhou
As Large Language Models (LLMs) continue to evolve, more are being designed to handle long-context inputs.
1 code implementation • 19 Sep 2023 • Ruiqi Xu, Yongfeng Huang, Xin Chen, Lin Zhang
In this work, we introduce the concept of complex text style transfer tasks, and constructed complex text datasets based on two widely applicable scenarios.
no code implementations • 20 Aug 2023 • Zhixiao Qi, Yijiong Yu, Meiqi Tu, Junyi Tan, Yongfeng Huang
In this paper, we propose a method for handling structured knowledge and scanned documents in incremental pre-training.
1 code implementation • 9 Aug 2023 • Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael R. Lyu, LiWei Wang
With the continuous emergence of Chinese Large Language Models (LLMs), how to evaluate a model's capabilities has become an increasingly significant issue.
no code implementations • 25 Jun 2023 • Tao Qi, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, Xing Xie
In this paper, instead of client uniform sampling, we propose a novel data uniform sampling strategy for federated learning (FedSampling), which can effectively improve the performance of federated learning especially when client data size distribution is highly imbalanced across clients.
1 code implementation • 7 Oct 2022 • Haoqin Tu, Zhongliang Yang, Jinshuai Yang, Siyu Zhang, Yongfeng Huang
Visualization of the local latent prior well confirms the primary devotion in hidden space of the proposed model.
1 code implementation • 7 Sep 2022 • Jiaxing Zhang, Ruyi Gan, Junjie Wang, Yuxiang Zhang, Lin Zhang, Ping Yang, Xinyu Gao, Ziwei Wu, Xiaoqun Dong, Junqing He, Jianheng Zhuo, Qi Yang, Yongfeng Huang, Xiayu Li, Yanghan Wu, Junyu Lu, Xinyu Zhu, Weifeng Chen, Ting Han, Kunhao Pan, Rui Wang, Hao Wang, XiaoJun Wu, Zhongshen Zeng, Chongpei Chen
We hope that this project will be the foundation of Chinese cognitive intelligence.
no code implementations • 11 Jun 2022 • Jia Li, Yongfeng Huang, Heng Chang, Yu Rong
We study the node classification problem in the hierarchical graph where a 'node' is a graph instance.
1 code implementation • 7 Jun 2022 • Tao Qi, Fangzhao Wu, Chuhan Wu, Lingjuan Lyu, Tong Xu, Zhongliang Yang, Yongfeng Huang, Xing Xie
In order to learn a fair unified representation, we send it to each platform storing fairness-sensitive features and apply adversarial learning to remove bias from the unified representation inherited from the biased data.
1 code implementation • Nature Communications 2022 • Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Tao Qi, Yongfeng Huang, Xing Xie
Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation.
Ranked #1 on
Recommendation Systems
on MovieLens 100K
(RMSE metric)
1 code implementation • 12 May 2022 • Haoqin Tu, Zhongliang Yang, Jinshuai Yang, Yongfeng Huang
Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving representation learning and generation for natural language at the same time.
no code implementations • 21 Apr 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie
In this paper, we propose a federated contrastive learning method named FedCL for privacy-preserving recommendation, which can exploit high-quality negative samples for effective model training with privacy well protected.
no code implementations • 10 Apr 2022 • Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang
Existing methods for news recommendation usually model user interest from historical clicked news without the consideration of candidate news.
1 code implementation • 10 Apr 2022 • Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang, Xing Xie
To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data.
no code implementations • 10 Apr 2022 • Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang
The core idea of FUM is to concatenate the clicked news into a long document and transform user modeling into a document modeling task with both intra-news and inter-news word-level interactions.
no code implementations • 1 Apr 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Since candidate news selection can be biased, we propose to use a shared candidate-aware user model to match user interest with a real displayed candidate news and a random news, respectively, to learn a candidate-aware user embedding that reflects user interest in candidate news and a candidate-invariant user embedding that indicates intrinsic user interest.
no code implementations • 1 Apr 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Different from existing news recommendation methods that are usually based on point- or pair-wise ranking, in LeaDivRec we propose a more effective list-wise news recommendation model.
no code implementations • 1 Apr 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
In addition, we weight the distillation loss based on the overall prediction correctness of the teacher ensemble to distill high-quality knowledge.
no code implementations • 1 Apr 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
In this paper, we propose a semi-supervised fair representation learning approach based on adversarial variational autoencoder, which can reduce the dependency of adversarial fair models on data with labeled sensitive attributes.
no code implementations • 28 Feb 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
In this paper, we propose a quality-aware news recommendation method named QualityRec that can effectively improve the quality of recommended news.
no code implementations • 28 Feb 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
They are usually learned on historical user behavior data to infer user interest and predict future user behaviors (e. g., clicks).
no code implementations • ACL 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie
In this paper, we propose a very simple yet effective method named NoisyTune to help better finetune PLMs on downstream tasks by adding some noise to the parameters of PLMs before fine-tuning.
no code implementations • 10 Feb 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yanlin Wang, Yuqing Yang, Yongfeng Huang, Xing Xie
To solve the game, we propose a platform negotiation method that simulates the bargaining among platforms and locally optimizes their policies via gradient descent.
no code implementations • 10 Feb 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie
However, existing general FL poisoning methods for degrading model performance are either ineffective or not concealed in poisoning federated recommender systems.
no code implementations • 6 Dec 2021 • Dehao Tao, Yingzhu Xiong, Zhongliang Yang, Yongfeng Huang
In recent years, text summarization methods have attracted much attention again thanks to the researches on neural network models.
no code implementations • Findings (EMNLP) 2021 • Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie
In this paper, we propose a unified news recommendation framework, which can utilize user data locally stored in user clients to train models and serve users in a privacy-preserving way.
no code implementations • 3 Sep 2021 • Chuhan Wu, Fangzhao Wu, Yang Yu, Tao Qi, Yongfeng Huang, Xing Xie
Two self-supervision tasks are incorporated in UserBERT for user model pre-training on unlabeled user behavior data to empower user modeling.
no code implementations • 30 Aug 2021 • Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, Xing Xie
Instead of directly communicating the large models between clients and server, we propose an adaptive mutual distillation framework to reciprocally learn a student and a teacher model on each client, where only the student model is shared by different clients and updated collaboratively to reduce the communication cost.
12 code implementations • 20 Aug 2021 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie
In this way, Fastformer can achieve effective context modeling with linear complexity.
Ranked #1 on
News Recommendation
on MIND
(using extra training data)
no code implementations • 20 Aug 2021 • Chuhan Wu, Fangzhao Wu, Tao Qi, Binxing Jiao, Daxin Jiang, Yongfeng Huang, Xing Xie
We then sample token pairs based on their probability scores derived from the sketched attention matrix to generate different sparse attention index matrices for different attention heads.
no code implementations • 20 Aug 2021 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
News recommendation is often modeled as a sequential recommendation task, which assumes that there are rich short-term dependencies over historical clicked news.
no code implementations • 16 Jun 2021 • Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges.
no code implementations • 11 Jun 2021 • Chuhan Wu, Fangzhao Wu, Yongfeng Huang
It is important to eliminate the effect of position biases on the recommendation model to accurately target user interests.
no code implementations • ACL 2021 • Tao Qi, Fangzhao Wu, Chuhan Wu, Peiru Yang, Yang Yu, Xing Xie, Yongfeng Huang
Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news.
1 code implementation • Findings (ACL) 2021 • Siyu Zhang, Zhongliang Yang, Jinshuai Yang, Yongfeng Huang
Generative linguistic steganography mainly utilized language models and applied steganographic sampling (stegosampling) to generate high-security steganographic text (stegotext).
no code implementations • Findings (ACL) 2021 • Chuhan Wu, Fangzhao Wu, Yongfeng Huang
In addition, we propose a multi-teacher hidden loss and a multi-teacher distillation loss to transfer the useful knowledge in both hidden states and soft labels from multiple teacher PLMs to the student model.
no code implementations • ACL 2021 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
It can effectively reduce the complexity and meanwhile capture global document context in the modeling of each sentence.
no code implementations • ACL 2021 • Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang
The former is used to capture the personalized user interest in news.
1 code implementation • 1 Jun 2021 • Yongfeng Huang, Yujun Chen, Yulun Du, Zhilin Yang
The task of rationalization aims to extract pieces of input text as rationales to justify neural network predictions on text classification tasks.
no code implementations • NAACL 2021 • Yubo Chen, Yunqi Zhang, Changran Hu, Yongfeng Huang
To explore entity pairs that may be implicitly connected by relations, we propose a binary pointer network to extract overlapping relational triples relevant to each word sequentially and retain the information of previously extracted triples in an external memory.
no code implementations • 27 May 2021 • Chuhan Wu, Fangzhao Wu, Yongfeng Huang
We estimate the optimal negative sampling ratio using the $K$ value that maximizes the training effectiveness function.
1 code implementation • 20 Apr 2021 • Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang
Our method interactively models candidate news and user interest to facilitate their accurate matching.
no code implementations • Findings (ACL) 2022 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Recall and ranking are two critical steps in personalized news recommendation.
1 code implementation • 15 Apr 2021 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Most of existing news representation methods learn news representations only from news texts while ignore the visual information in news like images.
1 code implementation • 15 Apr 2021 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Our PLM-empowered news recommendation models have been deployed to the Microsoft News platform, and achieved significant gains in terms of both click and pageview in both English-speaking and global markets.
no code implementations • Findings (EMNLP) 2021 • Chuhan Wu, Fangzhao Wu, Yang Yu, Tao Qi, Yongfeng Huang, Qi Liu
However, existing language models are pre-trained and distilled on general corpus like Wikipedia, which has some gaps with the news domain and may be suboptimal for news intelligence.
no code implementations • 9 Feb 2021 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Besides, the feed recommendation models trained solely on click behaviors cannot optimize other objectives such as user engagement.
no code implementations • 9 Feb 2021 • Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, Xing Xie
To incorporate high-order user-item interactions, we propose a user-item graph expansion method that can find neighboring users with co-interacted items and exchange their embeddings for expanding the local user-item graphs in a privacy-preserving way.
Ranked #2 on
Recommendation Systems
on MovieLens 100K
(RMSE metric)
no code implementations • 12 Jan 2021 • Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
The dwell time of news reading is an important clue for user interest modeling, since short reading dwell time usually indicates low and even negative interest.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Yubo Chen, Chuhan Wu, Tao Qi, Zhigang Yuan, Yongfeng Huang
In this paper, we propose a unified framework to incorporate multi-level contexts for named entity recognition.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
We learn user representations from browsed news representations, and compute click scores based on user and candidate news representations.
no code implementations • NAACL 2021 • Chuhan Wu, Fangzhao Wu, Yongfeng Huang
Since the raw weighted real distances may not be optimal for adjusting self-attention weights, we propose a learnable sigmoid function to map them into re-scaled coefficients that have proper ranges.
no code implementations • 8 Oct 2020 • Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
We propose a query-value interaction function which can learn query-aware attention values, and combine them with the original values and attention weights to form the final output.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Chuhan Wu, Fangzhao Wu, Tao Qi, Jianxun Lian, Yongfeng Huang, Xing Xie
Motivated by pre-trained language models which are pre-trained on large-scale unlabeled corpus to empower many downstream tasks, in this paper we propose to pre-train user models from large-scale unlabeled user behaviors data.
1 code implementation • 23 Jul 2020 • Chuhan Wu, Fangzhao Wu, Tao Di, Yongfeng Huang, Xing Xie
On each platform a local user model is used to learn user embeddings from the local user behaviors on that platform.
no code implementations • ACL 2020 • Chuhan Wu, Fangzhao Wu, Tao Qi, Xiaohui Cui, Yongfeng Huang
Different from existing pooling methods that use a fixed pooling norm, we propose to learn the norm in an end-to-end manner to automatically find the optimal ones for text representation in different tasks.
no code implementations • 30 Jun 2020 • Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, Xing Xie
In this paper, we propose a fairness-aware news recommendation approach with decomposed adversarial learning and orthogonality regularization, which can alleviate unfairness in news recommendation brought by the biases of sensitive user attributes.
no code implementations • 2 Jun 2020 • Zhongliang Yang, Baitao Gong, Yamin Li, Jinshuai Yang, Zhiwen Hu, Yongfeng Huang
On the one hand, we hide the secret information by coding the path in the knowledge graph, but not the conditional probability of each generated word; on the other hand, we can control the semantic expression of the generated steganographic text to a certain extent.
no code implementations • 31 Mar 2020 • Suyu Ge, Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Existing news recommendation methods achieve personalization by building accurate news representations from news content and user representations from their direct interactions with news (e. g., click), while ignoring the high-order relatedness between users and news.
2 code implementations • Findings of the Association for Computational Linguistics 2020 • Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie
Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.
no code implementations • 20 Mar 2020 • Suyu Ge, Fangzhao Wu, Chuhan Wu, Tao Qi, Yongfeng Huang, Xing Xie
Since the labeled data in different platforms usually has some differences in entity type and annotation criteria, instead of constraining different platforms to share the same model, we decompose the medical NER model in each platform into a shared module and a private module.
1 code implementation • 13 Nov 2019 • Zhongliang Yang, Ke Wang, Sai Ma, Yongfeng Huang, Xiangui Kang, Xianfeng Zhao
We hope that this test set can help to evaluate the robustness of steganalysis algorithms.
no code implementations • IJCNLP 2019 • Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie
In the review content-view, we propose to use a hierarchical model to first learn sentence representations from words, then learn review representations from sentences, and finally learn user/item representations from reviews.
4 code implementations • IJCNLP 2019 • Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, Xing Xie
The core of our approach is a news encoder and a user encoder.
no code implementations • IJCNLP 2019 • Chuhan Wu, Fangzhao Wu, Mingxiao An, Tao Qi, Jianqiang Huang, Yongfeng Huang, Xing Xie
In the user representation module, we propose an attentive multi-view learning framework to learn unified representations of users from their heterogeneous behaviors such as search queries, clicked news and browsed webpages.
no code implementations • WS 2019 • Suyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang
This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop.
6 code implementations • 12 Jul 2019 • Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie
In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning.
Ranked #6 on
News Recommendation
on MIND
no code implementations • 12 Jul 2019 • Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie
Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles.
no code implementations • ACL 2019 • Chuhan Wu, Fangzhao Wu, Mingxiao An, Yongfeng Huang, Xing Xie
The core of our approach is a topic-aware news encoder and a user encoder.
1 code implementation • 26 Jun 2019 • Sadaqat ur Rehman, Zhongliang Yang, Muhammad Shahid, Nan Wei, Yongfeng Huang, Muhammad Waqas, Shanshan Tu, Obaid ur Rehman
Water supplies are crucial for the development of living beings.
no code implementations • SEMEVAL 2019 • Suyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang
With the development of the Internet, dialog systems are widely used in online platforms to provide personalized services for their users.
no code implementations • SEMEVAL 2019 • Tao Qi, Suyu Ge, Chuhan Wu, Yubo Chen, Yongfeng Huang
First name: Tao Last name: Qi Email: taoqi. qt@gmail. com Affiliation: Department of Electronic Engineering, Tsinghua University First name: Suyu Last name: Ge Email: gesy17@mails. tsinghua. edu. cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Chuhan Last name: Wu Email: wuch15@mails. tsinghua. edu. cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Yubo Last name: Chen Email: chen-yb18@mails. tsinghua. edu. cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Yongfeng Last name: Huang Email: yfhuang@mail. tsinghua. edu. cn Affiliation: Department of Electronic Engineering, Tsinghua University Toponym resolution is an important and challenging task in the neural language processing field, and has wide applications such as emergency response and social media geographical event analysis.
no code implementations • NAACL 2019 • Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang
In this paper, we propose a hierarchical user and item representation model with three-tier attention to learn user and item representations from reviews for recommendation.
1 code implementation • 26 Apr 2019 • Fangzhao Wu, Junxin Liu, Chuhan Wu, Yongfeng Huang, Xing Xie
Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming.
Chinese Named Entity Recognition
named-entity-recognition
+1
no code implementations • 26 Apr 2019 • Junxin Liu, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie
Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS.
no code implementations • 4 Feb 2019 • Zhongliang Yang, Hao Yang, Yuting Hu, Yongfeng Huang, Yu-Jin Zhang
To solve these two challenges, in this paper, combined with the sliding window detection algorithm and Convolution Neural Network we propose a real-time VoIP steganalysis method which based on multi-channel convolution sliding windows.
1 code implementation • WS 2018 • Chuhan Wu, Fangzhao Wu, Junxin Liu, Sixing Wu, Yongfeng Huang, Xing Xie
This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions.
no code implementations • 11 Jul 2018 • Junxin Liu, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie
The experimental results on two benchmark datasets validate that our approach can effectively improve the performance of Chinese word segmentation, especially when training data is insufficient.
1 code implementation • SEMEVAL 2018 • Chuhan Wu, Fangzhao Wu, Sixing Wu, Junxin Liu, Zhigang Yuan, Yongfeng Huang
Detecting irony is an important task to mine fine-grained information from social web messages.
no code implementations • SEMEVAL 2018 • Chuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Junxin Liu, Yongfeng Huang
Thus, in SemEval-2018 Task 2 an interesting and challenging task is proposed, i. e., predicting which emojis are evoked by text-based tweets.
no code implementations • SEMEVAL 2018 • Chuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Yongfeng Huang
Thus, the aim of SemEval-2018 Task 10 is to predict whether a word is a discriminative attribute between two concepts.
no code implementations • SEMEVAL 2018 • Chuhan Wu, Fangzhao Wu, Junxin Liu, Zhigang Yuan, Sixing Wu, Yongfeng Huang
In order to address this task, we propose a system based on an attention CNN-LSTM model.
no code implementations • WS 2018 • Chuhan Wu, Fangzhao Wu, Yubo Chen, Sixing Wu, Zhigang Yuan, Yongfeng Huang
In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task.
1 code implementation • 23 Apr 2018 • Zhongliang Yang, Yongfeng Huang, Yiran Jiang, Yuxi Sun, Yu-Jin Zhan, Pengcheng Luo
Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP).
1 code implementation • IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2018 • Zinan Lin, Yongfeng Huang, Jilong Wang
Experiments show that on full embedding rate samples, RNN-SM is of high detection accuracy, which remains over 90% even when the sample is as short as 0. 1 s, and is significantly higher than other state-of-the-art methods.
no code implementations • IJCNLP 2017 • Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Sixing Wu, Zhigang Yuan
Since the existing valence-arousal resources of Chinese are mainly in word-level and there is a lack of phrase-level ones, the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) task aims to predict the valence-arousal ratings for Chinese affective words and phrases automatically.
no code implementations • ACL 2017 • Fangzhao Wu, Yongfeng Huang, Jun Yan
Instead of the source domain sentiment classifiers, our approach adapts the general-purpose sentiment lexicons to target domain with the help of a small number of labeled samples which are selected and annotated in an active learning mode, as well as the domain-specific sentiment similarities among words mined from unlabeled samples of target domain.
no code implementations • 8 Jun 2017 • Zhongliang Yang, Yu-Jin Zhang, Sadaqat ur Rehman, Yongfeng Huang
Automatically generating a natural language description of an image is a task close to the heart of image understanding.
no code implementations • 20 Mar 2017 • Yuting Hu, Liang Zheng, Yi Yang, Yongfeng Huang
Second, texts in these datasets are written in well-organized language, leading to inconsistency with realistic applications.