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 • 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.
1 code implementation • 21 Dec 2023 • Jingwei Yi, Yueqi Xie, Bin Zhu, Emre Kiciman, Guangzhong Sun, Xing Xie, Fangzhao Wu
Our analysis identifies two key factors contributing to their success: LLMs' inability to distinguish between informational context and actionable instructions, and their lack of awareness in avoiding the execution of instructions within external content.
1 code implementation • ICCV 2023 • Sungwon Han, Sungwon Park, Fangzhao Wu, Sundong Kim, Bin Zhu, Xing Xie, Meeyoung Cha
Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other.
1 code implementation • 18 Jul 2023 • Sungwon Park, Sungwon Han, Fangzhao Wu, Sundong Kim, Bin Zhu, Xing Xie, Meeyoung Cha
Evaluations of real-world scenarios across multiple datasets show that the proposed method enhances the robustness of federated learning against model poisoning attacks.
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 • 17 May 2023 • Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, Xing Xie
Companies have begun to offer Embedding as a Service (EaaS) based on these LLMs, which can benefit various natural language processing (NLP) tasks for customers.
1 code implementation • 4 Apr 2023 • Jiawei Shao, Fangzhao Wu, Jun Zhang
While federated learning is promising for privacy-preserving collaborative learning without revealing local data, it remains vulnerable to white-box attacks and struggles to adapt to heterogeneous clients.
1 code implementation • 15 Mar 2023 • Sungwon Han, Seungeon Lee, Fangzhao Wu, Sundong Kim, Chuhan Wu, Xiting Wang, Xing Xie, Meeyoung Cha
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications.
1 code implementation • 28 Feb 2023 • Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jaeboum Kim, Fangzhao Wu, Sunghun Kim
To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method.
1 code implementation • 13 Feb 2023 • Yuchen Liu, Chen Chen, Lingjuan Lyu, Fangzhao Wu, Sai Wu, Gang Chen
In order to address this issue, we propose GAS, a \shorten approach that can successfully adapt existing robust AGRs to non-IID settings.
no code implementations • 27 Dec 2022 • Zehua Sun, Yonghui Xu, Yong liu, wei he, Lanju Kong, Fangzhao Wu, Yali Jiang, Lizhen Cui
Federated learning has recently been applied to recommendation systems to protect user privacy.
no code implementations • 10 Nov 2022 • Yueqi Xie, Weizhong Zhang, Renjie Pi, Fangzhao Wu, Qifeng Chen, Xing Xie, Sunghun Kim
Since at each round, the number of tunable parameters optimized on the server side equals the number of participating clients (thus independent of the model size), we are able to train a global model with massive parameters using only a small amount of proxy data (e. g., around one hundred samples).
no code implementations • 20 Oct 2022 • Wei Yuan, Hongzhi Yin, Fangzhao Wu, Shijie Zhang, Tieke He, Hao Wang
It removes a user's contribution by rolling back and calibrating the historical parameter updates and then uses these updates to speed up federated recommender reconstruction.
1 code implementation • 17 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.
1 code implementation • 19 Jul 2022 • Sungwon Han, Sungwon Park, Fangzhao Wu, Sundong Kim, Chuhan Wu, Xing Xie, Meeyoung Cha
This paper presents FedX, an unsupervised federated learning framework.
1 code implementation • 26 Jun 2022 • Mengyan Zhang, Thanh Nguyen-Tang, Fangzhao Wu, Zhenyu He, Xing Xie, Cheng Soon Ong
We consider the problem of personalised news recommendation where each user consumes news in a sequential fashion.
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)
no code implementations • 22 May 2022 • Jingwei Yi, Fangzhao Wu, Huishuai Zhang, Bin Zhu, Tao Qi, Guangzhong Sun, Xing Xie
Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning framework.
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
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
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
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 • 16 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.
1 code implementation • 14 Feb 2022 • Jingwei Yi, Fangzhao Wu, Bin Zhu, Jing Yao, Zhulin Tao, Guangzhong Sun, Xing Xie
Our study reveals a critical security issue in existing federated news recommendation systems and calls for research efforts to address the issue.
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 • 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.
1 code implementation • 5 Dec 2021 • Xuanli He, Qiongkai Xu, Lingjuan Lyu, Fangzhao Wu, Chenguang Wang
Nowadays, due to the breakthrough in natural language generation (NLG), including machine translation, document summarization, image captioning, etc NLG models have been encapsulated in cloud APIs to serve over half a billion people worldwide and process over one hundred billion word generations per day.
1 code implementation • 2 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.
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.
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.
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.
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 • 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.
no code implementations • 23 May 2021 • Chen Chen, Xuanli He, Lingjuan Lyu, Fangzhao Wu
In this work, we bridge this gap by first presenting an effective model extraction attack, where the adversary can practically steal a BERT-based API (the target/victim model) by only querying a limited number of queries.
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
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.
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.
no code implementations • 15 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.
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 • 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 • 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 • 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.
no code implementations • ECAI 2020 • Sixing Wu, Fang Chen, Fangzhao Wu, Yongfeng Huang and Xing Li
In this paper, we propose a multi-task neural network to perform emotion-cause pair extraction in a unified model.
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.
2 code implementations • ACL 2020 • Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou
News recommendation is an important technique for personalized news service.
no code implementations • ACL 2020 • Heyuan Wang, Fangzhao Wu, Zheng Liu, Xing Xie
Existing studies generally represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation.
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 • 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.
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 • 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.
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 • 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 • 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.
Ranked #7 on
News Recommendation
on MIND
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 • 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.
no code implementations • 29 May 2019 • Xianchen Wang, Hongtao Liu, Peiyi Wang, Fangzhao Wu, Hongyan Xu, Wenjun Wang, Xing Xie
In this paper, we propose a hierarchical attention model fusing latent factor model for rating prediction with reviews, which can focus on important words and informative reviews.
5 code implementations • 29 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.
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.
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 • 9 Mar 2019 • Pengwei Wang, Dejing Dou, Fangzhao Wu, Nisansa de Silva, Lianwen Jin
And then, to put both triples and mined logic rules within the same semantic space, all triples in the knowledge graph are represented as first-order logic.
no code implementations • 9 Nov 2018 • Mingxiao An, Yongzhou Chen, Qi Liu, Chuanren Liu, Guangyi Lv, Fangzhao Wu, Jianhui Ma
Recently deep neural networks have been successfully used for various classification tasks, especially for problems with massive perfectly labeled training data.
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.
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 • 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 • 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.