Dingcheng Li


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Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Kaize Ding | Jianling Wang | Jundong Li | Dingcheng Li | Huan Liu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are: (1) unable to capture high-order interaction between words; (2) inefficient to handle large datasets and new documents. To address those issues, in this paper, we propose a principled model – hypergraph attention networks (HyperGAT), which can obtain more expressive power with less computational consumption for text representation learning. Extensive experiments on various benchmark datasets demonstrate the efficacy of the proposed approach on the text classification task.


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End-to-end Deep Reinforcement Learning Based Coreference Resolution
Hongliang Fei | Xu Li | Dingcheng Li | Ping Li
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent neural network models have significantly advanced the task of coreference resolution. However, current neural coreference models are usually trained with heuristic loss functions that are computed over a sequence of local decisions. In this paper, we introduce an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics. Specifically, we modify the state-of-the-art higher-order mention ranking approach in Lee et al. (2018) to a reinforced policy gradient model by incorporating the reward associated with a sequence of coreference linking actions. Furthermore, we introduce maximum entropy regularization for adequate exploration to prevent the model from prematurely converging to a bad local optimum. Our proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark.

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Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process
Dingcheng Li | Siamak Zamani | Jingyuan Zhang | Ping Li
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Leveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models. In this paper, we develop topic modeling with knowledge graph embedding (TMKGE), a Bayesian nonparametric model to employ knowledge graph (KG) embedding in the context of topic modeling, for extracting more coherent topics. Specifically, we build a hierarchical Dirichlet process (HDP) based model to flexibly borrow information from KG to improve the interpretability of topics. An efficient online variational inference method based on a stick-breaking construction of HDP is developed for TMKGE, making TMKGE suitable for large document corpora and KGs. Experiments on three public datasets illustrate the superior performance of TMKGE in terms of topic coherence and document classification accuracy, compared to state-of-the-art topic modeling methods.


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Representing Clinical Diagnostic Criteria in Quality Data Model Using Natural Language Processing
Na Hong | Dingcheng Li | Yue Yu | Hongfang Liu | Christopher G. Chute | Guoqian Jiang
Proceedings of BioNLP 15


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A Combination of Topic Models with Max-margin Learning for Relation Detection
Dingcheng Li | Swapna Somasundaran | Amit Chakraborty
Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing

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A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models
Dingcheng Li | Tim Miller | William Schuler
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Conditional Random Fields and Support Vector Machines for Disorder Named Entity Recognition in Clinical Texts
Dingcheng Li | Guergana Savova | Karin Kipper-Schuler
Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing