Search Results for author: Chuhan Wu

Found 87 papers, 25 papers with code

Named Entity Recognition with Context-Aware Dictionary Knowledge

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.

named-entity-recognition Named Entity Recognition +1

Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models

no code implementations25 Mar 2024 Yunjia Xi, Weiwen Liu, Jianghao Lin, Chuhan Wu, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu

The rise of large language models (LLMs) has opened new opportunities in Recommender Systems (RSs) by enhancing user behavior modeling and content understanding.

Language Modelling Large Language Model +1

Confidence-Aware Multi-Field Model Calibration

no code implementations27 Feb 2024 Yuang Zhao, Chuhan Wu, Qinglin Jia, Hong Zhu, Jia Yan, Libin Zong, Linxuan Zhang, Zhenhua Dong, Muyu Zhang

Calibration aims to address this issue by post-processing model predictions, and field-aware calibration can adjust model output on different feature field values to satisfy fine-grained advertising demands.

Learning to Edit: Aligning LLMs with Knowledge Editing

1 code implementation19 Feb 2024 Yuxin Jiang, YuFei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang

Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention.

knowledge editing Philosophy

A Unified Framework for Multi-Domain CTR Prediction via Large Language Models

1 code implementation17 Dec 2023 Zichuan Fu, Xiangyang Li, Chuhan Wu, Yichao Wang, Kuicai Dong, Xiangyu Zhao, Mengchen Zhao, Huifeng Guo, Ruiming Tang

Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them.

Click-Through Rate Prediction Language Modelling +2

Contrastive Multi-view Framework for Customer Lifetime Value Prediction

no code implementations26 Jun 2023 Chuhan Wu, Jingjie Li, Qinglin Jia, Hong Zhu, Yuan Fang, Ruiming Tang

Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications.

Contrastive Learning Marketing +1

How Can Recommender Systems Benefit from Large Language Models: A Survey

1 code implementation9 Jun 2023 Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Hao Zhang, Yong liu, Chuhan Wu, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, Weinan Zhang

In this paper, we conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.

Ethics Feature Engineering +5

Effective and Efficient Query-aware Snippet Extraction for Web Search

1 code implementation17 Oct 2022 Jingwei Yi, Fangzhao Wu, Chuhan Wu, Xiaolong Huang, Binxing Jiao, Guangzhong Sun, Xing Xie

In this paper, we propose an effective query-aware webpage snippet extraction method named DeepQSE, aiming to select a few sentences which can best summarize the webpage content in the context of input query.

Sentence

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

1 code implementation7 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.

Fairness Privacy Preserving +1

FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation

no code implementations21 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.

Contrastive Learning Privacy Preserving

News Recommendation with Candidate-aware User Modeling

no code implementations10 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.

News Recommendation

ProFairRec: Provider Fairness-aware News Recommendation

1 code implementation10 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.

Fairness News Recommendation

FUM: Fine-grained and Fast User Modeling for News Recommendation

no code implementations10 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.

News Recommendation

Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder

no code implementations1 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.

Attribute Fairness +1

Unified and Effective Ensemble Knowledge Distillation

no code implementations1 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.

Knowledge Distillation Transfer Learning

End-to-end Learnable Diversity-aware News Recommendation

no code implementations1 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.

News Recommendation

FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation

no code implementations1 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.

Attribute Fairness +1

Are Big Recommendation Models Fair to Cold Users?

no code implementations28 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).

Fairness Recommendation Systems

Quality-aware News Recommendation

no code implementations28 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.

News Recommendation

NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better

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 One Left Behind: Inclusive Federated Learning over Heterogeneous Devices

no code implementations16 Feb 2022 Ruixuan Liu, Fangzhao Wu, Chuhan Wu, Yanlin Wang, Lingjuan Lyu, Hong Chen, Xing Xie

In this way, all the clients can participate in the model learning in FL, and the final model can be big and powerful enough.

Federated Learning Knowledge Distillation +1

FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling

no code implementations10 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.

Federated Learning Recommendation Systems

Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction

no code implementations10 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.

Vertical Federated Learning

Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation

1 code implementation2 Dec 2021 Yang Yu, Fangzhao Wu, Chuhan Wu, Jingwei Yi, Qi Liu

We further propose a two-stage knowledge distillation method to improve the efficiency of the large PLM-based news recommendation model while maintaining its performance.

Knowledge Distillation Natural Language Understanding +1

Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation

1 code implementation EMNLP 2021 Jingwei Yi, Fangzhao Wu, Chuhan Wu, Ruixuan Liu, Guangzhong Sun, Xing Xie

However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients.

Federated Learning News Recommendation +1

Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving

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.

News Generation News Recommendation +2

UserBERT: Contrastive User Model Pre-training

no code implementations3 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.

FedKD: Communication Efficient Federated Learning via Knowledge Distillation

no code implementations30 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.

Federated Learning Knowledge Distillation

Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer

no code implementations20 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.

Fastformer: Additive Attention Can Be All You Need

9 code implementations20 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)

News Recommendation Text Classification +1

Is News Recommendation a Sequential Recommendation Task?

no code implementations20 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.

News Recommendation Sequential Recommendation

Personalized News Recommendation: Methods and Challenges

no code implementations16 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.

News Recommendation Recommendation Systems

DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning

no code implementations11 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.

News Recommendation Position

HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation

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.

News Recommendation

One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers

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.

Knowledge Distillation Language Modelling +1

Rethinking InfoNCE: How Many Negative Samples Do You Need?

no code implementations27 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.

Informativeness Mutual Information Estimation

Personalized News Recommendation with Knowledge-aware Interactive Matching

1 code implementation20 Apr 2021 Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang

Our method interactively models candidate news and user interest to facilitate their accurate matching.

Knowledge Graphs News Recommendation

MM-Rec: Multimodal News Recommendation

no code implementations15 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.

News Recommendation object-detection +1

DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation

no code implementations15 Apr 2021 Jingwei Yi, Fangzhao Wu, Chuhan Wu, Qifei Li, Guangzhong Sun, Xing Xie

The core of our method includes a bias representation module, a bias-aware user modeling module, and a bias-aware click prediction module.

News Recommendation

Empowering News Recommendation with Pre-trained Language Models

1 code implementation15 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.

Natural Language Understanding News Recommendation

FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

no code implementations9 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.

Privacy Preserving

FeedRec: News Feed Recommendation with Various User Feedbacks

no code implementations9 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.

News Recommendation

NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application

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.

Knowledge Distillation Language Modelling +2

Neural News Recommendation with Negative Feedback

no code implementations12 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.

News Recommendation

SentiRec: Sentiment Diversity-aware Neural News Recommendation

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.

News Recommendation

DA-Transformer: Distance-aware Transformer

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.

Improving Attention Mechanism with Query-Value Interaction

no code implementations8 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.

PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision

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.

FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data

1 code implementation23 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.

Click-Through Rate Prediction Privacy Preserving

Attentive Pooling with Learnable Norms for Text Representation

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.

FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning

no code implementations30 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.

Attribute Fairness +1

Graph Enhanced Representation Learning for News Recommendation

no code implementations31 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.

Graph Attention News Recommendation +1

FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning

no code implementations20 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.

Federated Learning Medical Named Entity Recognition +4

Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network

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.

MULTI-VIEW LEARNING Representation Learning +1

Neural News Recommendation with Heterogeneous User Behavior

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.

MULTI-VIEW LEARNING News Recommendation

Inverse Structural Design of Graphene/Boron Nitride Hybrids by Regressional GAN

1 code implementation21 Aug 2019 Yuan Dong, Dawei Li, Chi Zhang, Chuhan Wu, Hong Wang, Ming Xin, Jianlin Cheng, Jian Lin

A significant novelty of the proposed RGAN is that it combines the supervised and regressional convolutional neural network (CNN) with the traditional unsupervised GAN, thus overcoming the common technical barrier in the traditional GANs, which cannot generate data associated with given continuous quantitative labels.

Computational Physics Materials Science Applied Physics

Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention

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.

Language Modelling Task 2 +1

NPA: Neural News Recommendation with Personalized Attention

no code implementations12 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.

Informativeness News Recommendation

Neural News Recommendation with Attentive Multi-View Learning

5 code implementations12 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.

MULTI-VIEW LEARNING News Recommendation +2

Neural News Recommendation with Long- and Short-term User Representations

1 code implementation ACL 2019 Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie

In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations.

News Recommendation

THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN

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.

Emotion Classification Emotion Recognition +2

THU\_NGN at SemEval-2019 Task 12: Toponym Detection and Disambiguation on Scientific Papers

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.

POS Toponym Resolution +1

Hierarchical User and Item Representation with Three-Tier Attention for Recommendation

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.

Informativeness Recommendation Systems +1

NRPA: Neural Recommendation with Personalized Attention

5 code implementations29 May 2019 Hongtao Liu, Fangzhao Wu, Wenjun Wang, Xianchen Wang, Pengfei Jiao, Chuhan Wu, Xing Xie

In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews.

Informativeness News Recommendation +1

Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation

1 code implementation26 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

Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

no code implementations26 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.

Chinese Word Segmentation Segmentation

Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention

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.

Deep Learning Bandgaps of Topologically Doped Graphene

no code implementations28 Sep 2018 Yuan Dong, Chuhan Wu, Chi Zhang, Yingda Liu, Jianlin Cheng, Jian Lin

Moreover, given ubiquitous existence of topologies in materials, this work will stimulate widespread interests in applying deep learning algorithms to topological design of materials crossing atomic, nano-, meso-, and macro- scales.

Materials Science Computational Physics

Neural Chinese Word Segmentation with Dictionary Knowledge

no code implementations11 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.

Chinese Word Segmentation Multi-Task Learning +1

Neural Metaphor Detecting with 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.

Machine Translation POS +1

THU\_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM

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.

Opinion Mining POS +2

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