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Incorporating world knowledge to document clustering via heterogeneous information networks

Chenguang Wang, Yangqiu Song, Ahmed El-Kishky, Dan Roth, Ming Zhang, and Jiawei Han.

In Proc. 2015 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2015).

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We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network.

Text classification with heterogeneous information network kernels

Chenguang Wang, Yangqiu Song, Haoran Li, Ming Zhang and Jiawei Han.

In Proc. 2016 AAAI Conf. on Artificial Intelligence (AAAI 2016).

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This paper presents a novel text as network classification framework, which introduces a structured and typed heterogeneous information networks (HINs) representation of texts, and a meta-path based approach to link texts.

Crowd-in-the-loop: A hybrid approach for annotating semantic roles

Chenguang Wang, Alan Akbik, Laura Chiticariu, Yunyao Li, Fei Xia, and Anbang Xu.

In Proc. 2017 Conf. on Empirical Methods on Natural Language Processing (EMNLP 2017).

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Our experimental evaluation shows that the proposed approach reduces the workload for experts by over two-thirds, and thus significantly reduces the cost of producing SRL annotation at little loss in quality.

Language models with Transformers

Chenguang Wang, et al.

In arXiv preprint arXiv:1904.09408 (arXiv 2019).

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Gets more than 4.4k blog views and more than 320 Likes and Retweets on Twitter. Experimental results on the PTB, WikiText-2, and WikiText-103 show that proposed method achieves perplexities between 20.42 and 34.11 on all problems, i.e. on average an improvement of 12.0 perplexity units compared to state-of-the-art LSTMs.

Language Models are Open Knowledge Graphs

Chenguang Wang, Xiao Liu, and Dawn Song.

In arXiv preprint arXiv:2010.11967 (arXiv 2020).

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What's the relationship between deep language models (e.g., BERT, GPT-2, GPT-3) and knowledge graphs? Can we use the pre-trained deep language models to construct knowledge graphs? We find that we can construct knowledge graphs from the pre-trained language models. The generated knowledge graphs not only cover the knowledge already in existing knowledge graphs, such as Wikidata, but also feature open factual knowledge that is new.

publications

ENGtube: An integrated subtitle environment for ESL

Chi-Ho Li, Shujie Liu, Chenguang Wang, and Ming Zhou.

In MT Summit XIII: the Thirteenth Machine Translation Summit (MTSummit 2011).

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Paraphrasing adaptation for web search ranking

Chenguang Wang, Nan Duan, Ming Zhou, and Ming Zhang.

In Proc. 2013 Annual Meeting of the Association for Computational Linguistics (ACL 2013).

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Measuring domain influence in heterogeneous networks

Quan Liu, Chenguang Wang, and Ming Zhang.

In Proc. 2014 ACM Int. Conf. on Web Search and Data Mining Workshop on Diffusion Networks and Cascade Analytics (WSDM 2014 Workshop).

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Spectral label refinement for noisy and missing text labels

Yangqiu Song, Chenguang Wang, Ming Zhang, Hailong Sun, and Qiang Yang.

In Proc. 2015 AAAI Conf. on Artificial Intelligence (AAAI 2015).

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Incorporating world knowledge to document clustering via heterogeneous information networks

Chenguang Wang, Yangqiu Song, Ahmed El-Kishky, Dan Roth, Ming Zhang, and Jiawei Han.

In Proc. 2015 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2015).

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Constrained information-theoretic tripartite graph clustering to identify semantically similar relations

Chenguang Wang, Yangqiu Song, Dan Roth, Chi Wang, Jiawei Han, Heng Ji, and Ming Zhang.

In Proc. 2015 Int. Joint Conf. on Artificial Intelligence (IJCAI 2015).

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KnowSim: A document similarity measure on structured heterogeneous information networks

Chenguang Wang, Yangqiu Song, Haoran Li, Ming Zhang, and Jiawei Han.

In Proc. of 2015 IEEE Int. Conf. on Data Mining (ICDM 2015).

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Text classification with heterogeneous information network kernels

Chenguang Wang, Yangqiu Song, Haoran Li, Ming Zhang, and Jiawei Han.

In Proc. 2016 AAAI Conf. on Artificial Intelligence (AAAI 2016).

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RelSim: Relation similarity search in schema-rich heterogeneous information networks

Chenguang Wang, Yizhou Sun, Yanglei Song, Jiawei Han, Yangqiu Song, Lidan Wang, and Ming Zhang.

In Proc. 2016 SIAM Int. Conf. on Data Mining (SDM 2016)".

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World knowledge as indirect supervision for document clustering

Chenguang Wang, Yangqiu Song, Ahmed El-Kishky, Dan Roth, Ming Zhang, and Jiawei Han.

In ACM Transactions on Knowledge Discovery from Data (TKDD 2016).

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HINE: Heterogeneous information network embedding

Yuxin Chen, and Chenguang Wang.

In Proc. 2017 Int. Conf. on Database Systems for Advanced Applications (DASFAA 2017).

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Towards re-defining relation understanding in financial domain

Chenguang Wang, Doug Burdick, Laura Chiticariu, Rajasekar krishnamurthy, Yunyao Li, and Huaiyu Zhu.

In Proc. of 2017 ACM SIGMOD Int. Conf. on Management of Data Workshop (SIGMOD 2017 Workshop).

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Semi-supervised learning over heterogeneous information networks by ensemble of meta-graph guided random walks

He Jiang, Yangqiu Song, Chenguang Wang, Ming Zhang, and Yizhou Sun.

In Proc. 2017 Int. Joint Conf. on Artificial Intelligence (IJCAI 2017).

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Active learning for black-box semantic role labeling with neural factors

Chenguang Wang, Laura Chiticariu, and Yunyao Li.

In Proc. 2017 Int. Joint Conf. on Artificial Intelligence (IJCAI 2017).

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Crowd-in-the-loop: A hybrid approach for annotating semantic roles

Chenguang Wang, Alan Akbik, Laura Chiticariu, Yunyao Li, Fei Xia, and Anbang Xu.

In Proc. 2017 Conf. on Empirical Methods on Natural Language Processing (EMNLP 2017).

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Distant meta-path similarities for text-based heterogeneous information networks

Chenguang Wang, Yangqiu Song, Haoran Li, Yizhou Sun, Ming Zhang, and Jiawei Han.

In Proc. 2017 ACM Int. Conf. on Information and Knowledge Management (CIKM 2017).

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Unsupervised meta-path selection for similarity measure on heterogeneous information networks

Chenguang Wang, Yangqiu Song, Haoran Li, Ming Zhang, and Jiawei Han.

In Proc. 2018 Data Mining and Knowledge Discovery (DMKD 2018).

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Co-occurrent features in semantic segmentation

Hang Zhang, Han Zhang, Chenguang Wang, and Junyuan Xie.

In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2019).

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From shallow to deep language representations: Pre-training, fine-tuning, and beyond

Aston Zhang, Haibin Lin, Chenguang Wang, Mu Li, and Alexander Smola.

In Proc. 2019 ACM SIGKDD Int. Conf.on Knowledge Discovery and Data Mining (KDD 2019).

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Language models with Transformers

Chenguang Wang, Mu Li, and Alexander Smola.

In arXiv preprint arXiv:1904.09408 (arXiv 2019).

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Transformer on a diet

Chenguang Wang, Zihao Ye, Aston Zhang, Zheng Zhang, and Alexander Smola.

In arXiv preprint arXiv:2002.06170 (arXiv 2020).

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PoD: Positional dependency-based word embedding for aspect term extraction

Yichun Yin, Chenguang Wang, and Ming Zhang.

In Proc. 2020 Int. Conf. on Computational Linguistics (COLING 2020).

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GluonCV and GluonNLP: Deep learning in computer vision and natural language processing

Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, and Shuai Zheng.

In Journal of Machine Learning Research (JMLR 2020).

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Language models are open knowledge graphs

Chenguang Wang, Xiao Liu, and Dawn Song.

In arXiv preprint arXiv:2010.11967 (arXiv 2020).

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Zero-shot information extraction as a unified text-to-triple translation

Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, and Dawn Song.

In Proc. 2021 Conf. on Empirical Methods on Natural Language Processing (EMNLP 2021).

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Improving representation of the AOD to PM2.5 relationship with a convolutional neural network

Siyuan Shen, Aaron van Donkelaar, Randall V. Martin, Nathan Jacobs, and Chenguang Wang.

In Proc. 2022 Advancing Earth and Space Science (AGU 2022).

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Protecting intellectual property of language generation APIs with lexical watermark

Xuanli He, Qiongkai Xu, Lingjuan Lyu, Fangzhao Wu, and Chenguang Wang.

In Proc. 2022 AAAI Conf. on Artificial Intelligence (AAAI 2022).

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Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Zhiyuan Zhang, Lingjuan Lyu, Xingjun Ma, Chenguang Wang and Xu Sun.

In Proc. 2022 Conf. on Empirical Methods on Natural Language Processing (EMNLP 2022).

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Benchmarking Language Models for Code Syntax Understanding

Da Shen, Xinyun Chen*, Chenguang Wang*, Koushik Sen and Dawn Song.

In Proc. 2022 Conf. on Empirical Methods on Natural Language Processing (EMNLP 2022).

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PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion

Jianhao Shen, Chenguang Wang*, Ye Yuan, Jiawei Han, Heng Ji, Koushik Sen, Ming Zhang* and Dawn Song*.

In Proc. 2022 Conf. on Empirical Methods on Natural Language Processing (EMNLP 2022).

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IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models

Chenguang Wang, Xiao Liu and Dawn Song.

In Proc. 2022 Conf. on Empirical Methods on Natural Language Processing (EMNLP 2022).

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Joint language semantic and structure embedding for knowledge graph completion

Jianhao Shen, Chenguang Wang*, Linyuan Gong, and Dawn Song.

In Proc. 2022 Int. Conf. on Computational Linguistics (COLING 2022).

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DeepStruct: Pretraining of language models for structure prediction

Chenguang Wang*, Xiao Liu*, Zui Chen*, Haoyun Hong, Jie Tang, and Dawn Song.

In Proc. 2022 Annual Meeting of the Association for Computational Linguistics (ACL 2022).

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Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study

Myeongseob Ko, Ming Jin, Chenguang Wang, Ruoxi Jia.

In International Conf. on Computer Vision (ICCV 2023).

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CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models

Zhiyuan Yu, Yuhao Wu, Ning Zhang, Chenguang Wang, Yevgeniy Vorobeychik, Chaowei Xiao.

In Proc. of the 40th International Conf. on Machine Learning (ICML 2023).

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Agent Instructs Large Language Models to be General Zero-Shot Reasoners

Nicholas Crispino, Kyle Montgomery, Fankun Zeng, Dawn Song, and Chenguang Wang.

In arXiv preprint arXiv:2310.03710 (arXiv 2023).

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Measuring Vision-Language STEM Skills of Neural Models

Jianhao Shen, Ye Yuan, Srbuhi Mirzoyan, Ming Zhang, and Chenguang Wang.

In The Twelfth International Conf. on Learning Representations (ICLR 2024).

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Preference Poisoning Attacks on Reward Model Learning

Junlin Wu, Jiongxiao Wang, Chaowei Xiao, Chenguang Wang, Ning Zhang, Yevgeniy Vorobeychik.

In arXiv preprint arXiv:2402.01920 (arXiv 2024).

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