Zhongyu Wei


2021

pdf bib
基于多质心异质图学习的社交网络用户建模(User Representation Learning based on Multi-centroid Heterogeneous Graph Neural Networks)
Shangyi Ning (宁上毅) | Guanying Li (李冠颖) | Qin Chen (陈琴) | Zengfeng Huang (黄增峰) | Baohua Zhou (周葆华) | Zhongyu Wei (魏忠钰)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

“用户建模已经引起了学术界和工业界的广泛关注。现有的方法大多侧重于将用户间的人际关系融入社区,而用户生成的内容(如帖子)却没有得到很好的研究。在本文中,我们通过实际舆情传播相关的分析表明,在舆情传播过程中对用户属性进行研究的重要作用,并且提出了用户资料数据的筛选方法。同时,我们提出了一种通过异构多质心图池为用户捕获更多不同社区特征的建模。我们首先构造了一个由用户和关键字组成的异质图,并在其上采用了一个异质图神经网络。为了方便用户建模的图表示,提出了一种多质心图池化机制,将多质心的集群特征融入到表示学习中。在三个基准数据集上的大量实验表明了该方法的有效性。”

pdf bib
Mask Attention Networks: Rethinking and Strengthen Transformer
Zhihao Fan | Yeyun Gong | Dayiheng Liu | Zhongyu Wei | Siyuan Wang | Jian Jiao | Nan Duan | Ruofei Zhang | Xuanjing Huang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Transformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation. In this paper, we present a novel understanding of SAN and FFN as Mask Attention Networks (MANs) and show that they are two special cases of MANs with static mask matrices. However, their static mask matrices limit the capability for localness modeling in text representation learning. We therefore introduce a new layer named dynamic mask attention network (DMAN) with a learnable mask matrix which is able to model localness adaptively. To incorporate advantages of DMAN, SAN, and FFN, we propose a sequential layered structure to combine the three types of layers. Extensive experiments on various tasks, including neural machine translation and text summarization demonstrate that our model outperforms the original Transformer.

pdf bib
Discrete Argument Representation Learning for Interactive Argument Pair Identification
Lu Ji | Zhongyu Wei | Jing Li | Qi Zhang | Xuanjing Huang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In this paper, we focus on identifying interactive argument pairs from two posts with opposite stances to a certain topic. Considering opinions are exchanged from different perspectives of the discussing topic, we study the discrete representations for arguments to capture varying aspects in argumentation languages (e.g., the debate focus and the participant behavior). Moreover, we utilize hierarchical structure to model post-wise information incorporating contextual knowledge. Experimental results on the large-scale dataset collected from CMV show that our proposed framework can significantly outperform the competitive baselines. Further analyses reveal why our model yields superior performance and prove the usefulness of our learned representations.

pdf bib
Align Voting Behavior with Public Statements for Legislator Representation Learning
Xinyi Mou | Zhongyu Wei | Lei Chen | Shangyi Ning | Yancheng He | Changjian Jiang | Xuanjing Huang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Ideology of legislators is typically estimated by ideal point models from historical records of votes. It represents legislators and legislation as points in a latent space and shows promising results for modeling voting behavior. However, it fails to capture more specific attitudes of legislators toward emerging issues and is unable to model newly-elected legislators without voting histories. In order to mitigate these two problems, we explore to incorporate both voting behavior and public statements on Twitter to jointly model legislators. In addition, we propose a novel task, namely hashtag usage prediction to model the ideology of legislators on Twitter. In practice, we construct a heterogeneous graph for the legislative context and use relational graph neural networks to learn the representation of legislators with the guidance of historical records of their voting and hashtag usage. Experiment results indicate that our model yields significant improvements for the task of roll call vote prediction. Further analysis further demonstrates that legislator representation we learned captures nuances in statements.

pdf bib
Math Word Problem Solving with Explicit Numerical Values
Qinzhuo Wu | Qi Zhang | Zhongyu Wei | Xuanjing Huang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration. Most methods treat the numerical values in the problems as number symbols, and ignore the prominent role of the numerical values in solving the problem. In this paper, we propose a novel approach called NumS2T, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network. In addition, a numerical properties prediction mechanism is used to capture the category and comparison information of numerals and measure their importance in global expressions. Experimental results on the Math23K and APE datasets demonstrate that our model achieves better performance than existing state-of-the-art models.

pdf bib
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
Xiao Wang | Qin Liu | Tao Gui | Qi Zhang | Yicheng Zou | Xin Zhou | Jiacheng Ye | Yongxin Zhang | Rui Zheng | Zexiong Pang | Qinzhuo Wu | Zhengyan Li | Chong Zhang | Ruotian Ma | Zichu Fei | Ruijian Cai | Jun Zhao | Xingwu Hu | Zhiheng Yan | Yiding Tan | Yuan Hu | Qiyuan Bian | Zhihua Liu | Shan Qin | Bolin Zhu | Xiaoyu Xing | Jinlan Fu | Yue Zhang | Minlong Peng | Xiaoqing Zheng | Yaqian Zhou | Zhongyu Wei | Xipeng Qiu | Xuanjing Huang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

TextFlint is a multilingual robustness evaluation toolkit for NLP tasks that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. This enables practitioners to automatically evaluate their models from various aspects or to customize their evaluations as desired with just a few lines of code. TextFlint also generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model in terms of its robustness. To guarantee acceptability, all the text transformations are linguistically based and all the transformed data selected (up to 100,000 texts) scored highly under human evaluation. To validate the utility, we performed large-scale empirical evaluations (over 67,000) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. The toolkit is already available at https://github.com/textflint with all the evaluation results demonstrated at textflint.io.

pdf bib
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Ruize Wang | Duyu Tang | Nan Duan | Zhongyu Wei | Xuanjing Huang | Jianshu Ji | Guihong Cao | Daxin Jiang | Ming Zhou
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

pdf bib
Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification
Jian Yuan | Zhongyu Wei | Donghua Zhao | Qi Zhang | Changjian Jiang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

pdf bib
An Enhanced Knowledge Injection Model for Commonsense Generation
Zhihao Fan | Yeyun Gong | Zhongyu Wei | Siyuan Wang | Yameng Huang | Jian Jiao | Xuanjing Huang | Nan Duan | Ruofei Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts. Digging the relationship of concepts from scratch is non-trivial, therefore, we retrieve prototypes from external knowledge to assist the understanding of the scenario for better description generation. We integrate two additional modules into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure. We conduct experiment on CommonGen benchmark, experimental results show that our method significantly improves the performance on all the metrics.

pdf bib
Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication
Ruize Wang | Zhongyu Wei | Ying Cheng | Piji Li | Haijun Shan | Ji Zhang | Qi Zhang | Xuanjing Huang
Proceedings of the 28th International Conference on Computational Linguistics

Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we propose a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream. A story is then constructed with the guidance of the topic description. In order to combine the two generation tasks, we propose a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism. We validate our approach on VIST dataset, where quantitative results, ablations, and human evaluation demonstrate our method’s good ability in generating stories with higher quality compared to state-of-the-art methods.

pdf bib
Modeling Evolution of Message Interaction for Rumor Resolution
Lei Chen | Zhongyu Wei | Jing Li | Baohua Zhou | Qi Zhang | Xuanjing Huang
Proceedings of the 28th International Conference on Computational Linguistics

Previous work for rumor resolution concentrates on exploiting time-series characteristics or modeling topology structure separately. However, how local interactive pattern affects global information assemblage has not been explored. In this paper, we attempt to address the problem by learning evolution of message interaction. We model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. Moreover, we capture the variation of message interaction using a hierarchical framework to better integrate information flow of a rumor cascade. Experiments on PHEME dataset demonstrate our proposed model achieves higher accuracy than existing methods.

pdf bib
Automatic Term Name Generation for Gene Ontology: Task and Dataset
Yanjian Zhang | Qin Chen | Yiteng Zhang | Zhongyu Wei | Yixu Gao | Jiajie Peng | Zengfeng Huang | Weijian Sun | Xuanjing Huang
Findings of the Association for Computational Linguistics: EMNLP 2020

Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine. Most previous research focuses on inferring new GO terms, while the term names that reflect the gene function are still named by the experts. To fill this gap, we propose a novel task, namely term name generation for GO, and build a large-scale benchmark dataset. Furthermore, we present a graph-based generative model that incorporates the relations between genes, words and terms for term name generation, which exhibits great advantages over the strong baselines.

pdf bib
Simplify the Usage of Lexicon in Chinese NER
Ruotian Ma | Minlong Peng | Qi Zhang | Zhongyu Wei | Xuanjing Huang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recently, many works have tried to augment the performance of Chinese named entity recognition (NER) using word lexicons. As a representative, Lattice-LSTM has achieved new benchmark results on several public Chinese NER datasets. However, Lattice-LSTM has a complex model architecture. This limits its application in many industrial areas where real-time NER responses are needed. In this work, we propose a simple but effective method for incorporating the word lexicon into the character representations. This method avoids designing a complicated sequence modeling architecture, and for any neural NER model, it requires only subtle adjustment of the character representation layer to introduce the lexicon information. Experimental studies on four benchmark Chinese NER datasets show that our method achieves an inference speed up to 6.15 times faster than those of state-of-the-art methods, along with a better performance. The experimental results also show that the proposed method can be easily incorporated with pre-trained models like BERT.

pdf bib
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection
Ruize Wang | Duyu Tang | Nan Duan | Wanjun Zhong | Zhongyu Wei | Xuanjing Huang | Daxin Jiang | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The former refers to the logical consistency between coarse- and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions. The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.

pdf bib
PathQG: Neural Question Generation from Facts
Siyuan Wang | Zhongyu Wei | Zhihao Fan | Zengfeng Huang | Weijian Sun | Qi Zhang | Xuanjing Huang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Existing research for question generation encodes the input text as a sequence of tokens without explicitly modeling fact information. These models tend to generate irrelevant and uninformative questions. In this paper, we explore to incorporate facts in the text for question generation in a comprehensive way. We present a novel task of question generation given a query path in the knowledge graph constructed from the input text. We divide the task into two steps, namely, query representation learning and query-based question generation. We formulate query representation learning as a sequence labeling problem for identifying the involved facts to form a query and employ an RNN-based generator for question generation. We first train the two modules jointly in an end-to-end fashion, and further enforce the interaction between these two modules in a variational framework. We construct the experimental datasets on top of SQuAD and results show that our model outperforms other state-of-the-art approaches, and the performance margin is larger when target questions are complex. Human evaluation also proves that our model is able to generate relevant and informative questions.

2019

pdf bib
A Lexicon-Based Graph Neural Network for Chinese NER
Tao Gui | Yicheng Zou | Qi Zhang | Minlong Peng | Jinlan Fu | Zhongyu Wei | Xuanjing Huang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success. However, the characteristic of chain structure and the lack of global semantics determine that RNN-based models are vulnerable to word ambiguities. In this work, we try to alleviate this problem by introducing a lexicon-based graph neural network with global semantics, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency. Based on the multiple graph-based interactions among characters, potential words, and the whole-sentence semantics, word ambiguities can be effectively tackled. Experiments on four NER datasets show that the proposed model achieves significant improvements against other baseline models.

pdf bib
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph
Xinzhu Lin | Xiahui He | Qin Chen | Huaixiao Tou | Zhongyu Wei | Ting Chen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Symptom diagnosis is a challenging yet profound problem in natural language processing. Most previous research focus on investigating the standard electronic medical records for symptom diagnosis, while the dialogues between doctors and patients that contain more rich information are not well studied. In this paper, we first construct a dialogue symptom diagnosis dataset based on an online medical forum with a large amount of dialogues between patients and doctors. Then, we provide some benchmark models on this dataset to boost the research of dialogue symptom diagnosis. In order to further enhance the performance of symptom diagnosis over dialogues, we propose a global attention mechanism to capture more symptom related information, and build a symptom graph to model the associations between symptoms rather than treating each symptom independently. Experimental results show that both the global attention and symptom graph are effective to boost dialogue symptom diagnosis. In particular, our proposed model achieves the state-of-the-art performance on the constructed dataset.

pdf bib
Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template
Yunfan Gu | Yang Yuqiao | Zhongyu Wei
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

Question paraphrasing aims to restate a given question with different expressions but keep the original meaning. Recent approaches are mostly based on neural networks following a sequence-to-sequence fashion, however, these models tend to generate unpredictable results. To overcome this drawback, we propose a pipeline model based on templates. It follows three steps, a) identifies template from the input question, b) retrieves candidate templates, c) fills candidate templates with original topic words. Experiment results on two self-constructed datasets show that our model outperforms the sequence-to-sequence model in a large margin and the advantage is more promising when the size of training sample is small.

pdf bib
Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning
Zhihao Fan | Zhongyu Wei | Siyuan Wang | Xuanjing Huang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Image Captioning aims at generating a short description for an image. Existing research usually employs the architecture of CNN-RNN that views the generation as a sequential decision-making process and the entire dataset vocabulary is used as decoding space. They suffer from generating high frequent n-gram with irrelevant words. To tackle this problem, we propose to construct an image-grounded vocabulary, based on which, captions are generated with limitation and guidance. In specific, a novel hierarchical structure is proposed to construct the vocabulary incorporating both visual information and relations among words. For generation, we propose a word-aware RNN cell incorporating vocabulary information into the decoding process directly. Reinforce algorithm is employed to train the generator using constraint vocabulary as action space. Experimental results on MS COCO and Flickr30k show the effectiveness of our framework compared to some state-of-the-art models.

2018

pdf bib
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators
Zhihao Fan | Zhongyu Wei | Siyuan Wang | Yang Liu | Xuanjing Huang
Proceedings of the 27th International Conference on Computational Linguistics

Visual Question Generation (VQG) aims to ask natural questions about an image automatically. Existing research focus on training model to fit the annotated data set that makes it indifferent from other language generation tasks. We argue that natural questions need to have two specific attributes from the perspectives of content and linguistic respectively, namely, natural and human-written. Inspired by the setting of discriminator in adversarial learning, we propose two discriminators, one for each attribute, to enhance the training. We then use the reinforcement learning framework to incorporate scores from the two discriminators as the reward to guide the training of the question generator. Experimental results on a benchmark VQG dataset show the effectiveness and robustness of our model compared to some state-of-the-art models in terms of both automatic and human evaluation metrics.

pdf bib
Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model
Lu Ji | Zhongyu Wei | Xiangkun Hu | Yang Liu | Qi Zhang | Xuanjing Huang
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments. Most of the existing research explores different text features of reply comments on word level and ignores interactions between participants. In general, viewpoints are usually expressed by multiple arguments and exchanged on argument level. To better model the process of dialogical argumentation, we propose a novel co-attention mechanism based neural network to capture the interactions between participants on argument level. Experimental results on a publicly available dataset show that the proposed model significantly outperforms some state-of-the-art methods for persuasiveness evaluation. Further analysis reveals that attention weights computed in our model are able to extract interactive argument pairs from the original post and the reply.

pdf bib
A Joint Model of Conversational Discourse Latent Topics on Microblogs
Jing Li | Yan Song | Zhongyu Wei | Kam-Fai Wong
Computational Linguistics, Volume 44, Issue 4 - December 2018

Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurrence patterns. To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities of messages with varying discourse roles in containing topical words, our model is able to discover clusters of discourse words that are indicative of topical content. In an automatic evaluation on large-scale microblog corpora, our joint model yields topics with better coherence scores than competitive topic models from previous studies. Qualitative analysis on model outputs indicates that our model induces meaningful representations for both discourse and topics. We further present an empirical study on microblog summarization based on the outputs of our joint model. The results show that the jointly modeled discourse and topic representations can effectively indicate summary-worthy content in microblog conversations.

pdf bib
Task-oriented Dialogue System for Automatic Diagnosis
Zhongyu Wei | Qianlong Liu | Baolin Peng | Huaixiao Tou | Ting Chen | Xuanjing Huang | Kam-fai Wong | Xiangying Dai
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

In this paper, we make a move to build a dialogue system for automatic diagnosis. We first build a dataset collected from an online medical forum by extracting symptoms from both patients’ self-reports and conversational data between patients and doctors. Then we propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. Experimental results on our dataset show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and our dialogue system is able to collect these symptoms automatically and make a better diagnosis.

pdf bib
Incorporating Topic Aspects for Online Comment Convincingness Evaluation
Yunfan Gu | Zhongyu Wei | Maoran Xu | Hao Fu | Yang Liu | Xuanjing Huang
Proceedings of the 5th Workshop on Argument Mining

In this paper, we propose to incorporate topic aspects information for online comments convincingness evaluation. Our model makes use of graph convolutional network to utilize implicit topic information within a discussion thread to assist the evaluation of convincingness of each single comment. In order to test the effectiveness of our proposed model, we annotate topic information on top of a public dataset for argument convincingness evaluation. Experimental results show that topic information is able to improve the performance for convincingness evaluation. We also make a move to detect topic aspects automatically.

pdf bib
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning
Yucheng Wang | Zhongyu Wei | Yaqian Zhou | Xuanjing Huang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Automatic essay scoring (AES) is the task of assigning grades to essays without human interference. Existing systems for AES are typically trained to predict the score of each single essay at a time without considering the rating schema. In order to address this issue, we propose a reinforcement learning framework for essay scoring that incorporates quadratic weighted kappa as guidance to optimize the scoring system. Experiment results on benchmark datasets show the effectiveness of our framework.

2016

pdf bib
Is This Post Persuasive? Ranking Argumentative Comments in Online Forum
Zhongyu Wei | Yang Liu | Yi Li
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

pdf bib
An Efficient Cross-lingual Model for Sentence Classification Using Convolutional Neural Network
Yandi Xia | Zhongyu Wei | Yang Liu
Proceedings of the ACL 2016 Student Research Workshop

pdf bib
A Preliminary Study of Disputation Behavior in Online Debating Forum
Zhongyu Wei | Yandi Xia | Chen Li | Yang Liu | Zachary Stallbohm | Yi Li | Yang Jin
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

pdf bib
Using Relevant Public Posts to Enhance News Article Summarization
Chen Li | Zhongyu Wei | Yang Liu | Yang Jin | Fei Huang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article. In this paper we explore using public posts following a new article to improve automatic summary generation for the news article. We propose different approaches to incorporate information from public posts, including using frequency information from the posts to re-estimate bigram weights in the ILP-based summarization model and to re-weight a dependency tree edge’s importance for sentence compression, directly selecting sentences from posts as the final summary, and finally a strategy to combine the summarization results generated from news articles and posts. Our experiments on data collected from Facebook show that relevant public posts provide useful information and can be effectively leveraged to improve news article summarization results.

2015

pdf bib
Using Content-level Structures for Summarizing Microblog Repost Trees
Jing Li | Wei Gao | Zhongyu Wei | Baolin Peng | Kam-Fai Wong
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

pdf bib
Using Tweets to Help Sentence Compression for News Highlights Generation
Zhongyu Wei | Yang Liu | Chen Li | Wei Gao
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

pdf bib
Utilizing Microblogs for Automatic News Highlights Extraction
Zhongyu Wei | Wei Gao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

pdf bib
The CUHK Discourse TreeBank for Chinese: Annotating Explicit Discourse Connectives for the Chinese TreeBank
Lanjun Zhou | Binyang Li | Zhongyu Wei | Kam-Fai Wong
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The lack of open discourse corpus for Chinese brings limitations for many natural language processing tasks. In this work, we present the first open discourse treebank for Chinese, namely, the Discourse Treebank for Chinese (DTBC). At the current stage, we annotated explicit intra-sentence discourse connectives, their corresponding arguments and senses for all 890 documents of the Chinese Treebank 5. We started by analysing the characteristics of discourse annotation for Chinese, adapted the annotation scheme of Penn Discourse Treebank 2 (PDTB2) to Chinese language while maintaining the compatibility as far as possible. We made adjustments to 3 essential aspects according to the previous study of Chinese linguistics. They are sense hierarchy, argument scope and semantics of arguments. Agreement study showed that our annotation scheme could achieve highly reliable results.

pdf bib
Exploiting Community Emotion for Microblog Event Detection
Gaoyan Ou | Wei Chen | Tengjiao Wang | Zhongyu Wei | Binyang Li | Dongqing Yang | Kam-Fai Wong
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

pdf bib
Web Information Mining and Decision Support Platform for the Modern Service Industry
Binyang Li | Lanjun Zhou | Zhongyu Wei | Kam-fai Wong | Ruifeng Xu | Yunqing Xia
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2013

pdf bib
An Empirical Study on Uncertainty Identification in Social Media Context
Zhongyu Wei | Junwen Chen | Wei Gao | Binyang Li | Lanjun Zhou | Yulan He | Kam-Fai Wong
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

pdf bib
Quantising Opinions for Political Tweets Analysis
Yulan He | Hassan Saif | Zhongyu Wei | Kam-Fai Wong
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues. We analyzed tweet messages crawled during the eight weeks leading to the UK General Election in May 2010 and found that activities at Twitter is not necessarily a good predictor of popularity of political parties. We then proceed to propose a statistical model for sentiment detection with side information such as emoticons and hash tags implying tweet polarities being incorporated. Our results show that sentiment analysis based on a simple keyword matching against a sentiment lexicon or a supervised classifier trained with distant supervision does not correlate well with the actual election results. However, using our proposed statistical model for sentiment analysis, we were able to map the public opinion in Twitter with the actual offline sentiment in real world.

pdf bib
Cross-Lingual Identification of Ambiguous Discourse Connectives for Resource-Poor Language
Lanjun Zhou | Wei Gao | Binyang Li | Zhongyu Wei | Kam-Fai Wong
Proceedings of COLING 2012: Posters

2011

pdf bib
Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities
Lanjun Zhou | Binyang Li | Wei Gao | Zhongyu Wei | Kam-Fai Wong
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing