/KnowledgeGraphCourse

东南大学《知识图谱》研究生课程

A systematic course about knowledge graph for graduate students, interested researchers and engineers.

东南大学《知识图谱》研究生课程
时间：2019年春季（2月下旬~5月中旬）
每周五下午2:00~4:30
地点：东南大学九龙湖校区, 纪忠楼Y205
答疑/讨论/建议：请致信 pwang AT seu.edu.cn

课程内容

第1讲 知识图谱概论 （2019-3-1,2019-3-8）

1.1 知识图谱起源和发展
1.2 知识图谱 VS 深度学习
1.3 知识图谱 VS 关系数据库 VS 传统专家库
1.4 知识图谱本质和核心价值
1.5 知识图谱技术体系
1.6 典型知识图谱
1.7 知识图谱应用场景
课件下载:partA partB partC

第2讲 知识表示 (2019-3-15)

2.1 知识表示概念
2.2 知识表示方法

  • 语义网络
  • 产生式系统
  • 框架系统
  • 概念图
  • 形式化概念分析
  • 描述逻辑
  • 本体
  • 本体语言
  • 统计表示学习

课件下载:partA

第3讲 知识建模 （2019-3-15，2019-3-22）

3.1 本体
3.2 知识建模方法

  • 本体工程
  • 本体学习
  • 知识建模工具
  • 知识建模实践

课件下载:partA

第4讲 知识抽取基础:问题和方法（2019-3-22）

4.1 知识抽取场景
4.2 知识抽取挑战
4.3 面向结构化数据的知识抽取
4.4 面向半结构化数据的知识抽取
4.5 面向非机构化数据的知识抽取

课件下载:partA

第5讲 知识抽取:数据采集（2019-3-29）

5.1 数据采集原理和技术

  • 爬虫原理
  • 请求和响应
  • 多线程并行爬取
  • 反爬机制应对
    5.2 数据采集实践
  • 百科 论坛 社交网络等爬取实践

课件下载:partA

第6讲 知识抽取:实体识别（2019-3-29）

6.1 实体识别基本概念
6.2 基于规则和词典的实体识别方法
6.3 基于机器学习的实体识别方法
6.4 基于深度学习的实体识别方法
6.5 基于半监督学习的实体识别方法
6.6 基于迁移学习的实体识别方法
6.7 基于预训练的实体识别方法

课件下载:partA

第7讲 知识抽取:关系抽取（2019-4-19，2019-4-26）

7.1 关系基本概念
7.2 语义关系
7.3 关系抽取的特征
7.4 关系抽取数据集
7.5 基于监督学习的关系抽取方法
7.6 基于无监督学习的关系抽取方法
7.7 基于远程监督的关系抽取方法
7.8 基于深度学习/强化学习的关系抽取方法

课件下载:partA

第8讲 知识抽取:事件抽取（2019-3-29）

8.1 事件抽取基本概念
8.2 基于规则和模板的事件抽取方法
8.3 基于机器学习的事件抽取方法
8.4 基于深度学习的事件抽取方法
8.5 基于知识库的事件抽取方法
8.6 基于强化学习的事件抽取方法

课件下载:partA

附录A：经典文献选读

知识图谱构建

  1. Dong X, Gabrilovich E, Heitz G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. KDD2014: 601-610.
  2. Suchanek F M, Kasneci G, Weikum G. Yago: a core of semantic knowledge. WWW2007: 697-706.
  3. Hoffart J, Suchanek F M, Berberich K, et al. YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia. Artificial Intelligence, 2013, 194: 28-61.
  4. Navigli R, Ponzetto S P. BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence, 2012, 193: 217-250.
  5. Auer S, Bizer C, Kobilarov G, et al. Dbpedia: A nucleus for a web of open data. ISWC2007: 722-735.
  6. Mitchell T, Cohen W, Hruschka E, et al. Never-ending learning. Communications of the ACM, 2018, 61(5): 103-115. earlier work

知识表示和建模

  1. Sowa J F. Knowledge representation: logical, philosophical, and computational foundations. 1999.
  2. Noy N F, McGuinness D L. Ontology Development 101: A Guide to Creating Your First Ontology. another version

知识抽取

  • 信息抽取
  1. Etzioni O, Cafarella M, Downey D, et al. Web-scale information extraction in knowitall:(preliminary results).WWW2004: 100-110.
  2. Banko M, Cafarella M J, Soderland S, et al. Open information extraction from the web. IJCAI2007, 7: 2670-2676.
  3. Sarawagi S. Information extraction. Foundations and Trends® in Databases, 2008, 1(3): 261-377.
  4. Fader A, Soderland S, Etzioni O. Identifying relations for open information extraction. EMNLP2011: 1535-1545.
  5. Fan J, Kalyanpur A, Gondek D C, et al. Automatic knowledge extraction from documents. IBM Journal of Research and Development, 2012, 56(3.4): 5: 1-5: 10.
  6. Hearst M A. Automatic acquisition of hyponyms from large text corpora. ACL1992: 539-545.
  • 实体识别
  1. Nadeau D, Sekine S. A survey of named entity recognition and classification. Lingvisticae Investigationes, 2007, 30(1): 3-26.
  2. Lample G, Ballesteros M, Subramanian S, et al. Neural architectures for named entity recognition. NAACL-HLT 2016.
  3. Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991, 2015.
  4. Alhelbawy A, Gaizauskas R. Graph ranking for collective named entity disambiguation. ACL2014, 2: 75-80.
  5. Florian R, Ittycheriah A, Jing H, et al. Named entity recognition through classifier combination. HLT-NAACL2003: 168-171.
  6. Chiu J P C, Nichols E. Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics, 2016, 4: 357-370.
  7. Nothman J, Ringland N, Radford W, et al. Learning multilingual named entity recognition from Wikipedia. Artificial Intelligence, 2013, 194: 151-175.
  8. Santos C N, Guimaraes V. Boosting named entity recognition with neural character embeddings. Proceedings of NEWS 2015 The Fifth Named Entities Workshop, 2015.
  9. Chiticariu L, Krishnamurthy R, Li Y, et al. Domain adaptation of rule-based annotators for named-entity recognition tasks. EMNLP2010: 1002-1012.
  10. Shaalan K. A survey of arabic named entity recognition and classification. Computational Linguistics, 2014, 40(2): 469-510.
  11. Speck R, Ngomo A C N. Ensemble learning for named entity recognition. ISWC2014:519-534.
  12. Habibi M, Weber L, Neves M, et al. Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics, 2017, 33(14): i37-i48.
  • 关系抽取
  1. Wang C, Kalyanpur A, Fan J, et al. Relation extraction and scoring in DeepQA. IBM Journal of Research and Development, 2012, 56(3.4): 9: 1-9: 12.
  • 事件抽取
  1. Chen Y, Xu L, Liu K, et al. Event extraction via dynamic multi-pooling convolutional neural networks. ACL2015, 1: 167-176.
  2. Nguyen T H, Grishman R. Event detection and domain adaptation with convolutional neural networks. ACL2015, 2: 365-371.
  3. Hogenboom F, Frasincar F, Kaymak U, et al. An overview of event extraction from text. DeRiVE2011.
  4. Narasimhan K, Yala A, Barzilay R. Improving information extraction by acquiring external evidence with reinforcement learning. EMNLP2016.
  5. Nguyen T H, Cho K, Grishman R. Joint event extraction via recurrent neural networks. NAACL2016: 300-309.

知识融合

  1. Shvaiko P, Euzenat J. Ontology matching: state of the art and future challenges. IEEE Transactions on knowledge and data engineering, 2013, 25(1): 158-176.
  2. Noy N F, Musen M A. Algorithm and tool for automated ontology merging and alignment. AAAI2000.
  3. Do H H, Rahm E. COMA: a system for flexible combination of schema matching approaches.VLDB2002: 610-621.
  4. Doan A H, Madhavan J, Domingos P, et al. Learning to map between ontologies on the semantic web. WWW2002: 662-673.
  5. Ehrig M, Staab S. QOM–quick ontology mapping. ISWC2004: 683-697.
  6. Qu Y, Hu W, Cheng G. Constructing virtual documents for ontology matching. WWW2006: 23-31.
  7. Li J, Tang J, Li Y, et al. RiMOM: A dynamic multistrategy ontology alignment framework. IEEE Transactions on Knowledge and data Engineering, 2009, 21(8): 1218-1232.
  8. Mao M, Peng Y, Spring M. An adaptive ontology mapping approach with neural network based constraint satisfaction. Journal of Web Semantics, 2010, 8(1): 14-25.
  9. Hu W, Qu Y, Cheng G. Matching large ontologies: A divide-and-conquer approach. Data & Knowledge Engineering, 2008, 67(1): 140-160.
  10. Papadakis G, Ioannou E, Palpanas T, et al. A blocking framework for entity resolution in highly heterogeneous information spaces. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(12): 2665-2682.
  11. Wang P, Zhou Y, Xu B. Matching large ontologies based on reduction anchors. Twenty-Second International Joint Conference on Artificial Intelligence. 2011.
  12. Niu X, Rong S, Wang H, et al. An effective rule miner for instance matching in a web of data. CIKM2012: 1085-1094.
  13. Papadakis G, Ioannou E, Palpanas T, et al. A blocking framework for entity resolution in highly heterogeneous information spaces. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(12): 2665-2682.
  14. Li J, Wang Z, Zhang X, et al. Large scale instance matching via multiple indexes and candidate selection. Knowledge-Based Systems, 2013, 50: 112-120.
  15. Hu W, Chen J, Qu Y. A self-training approach for resolving object coreference on the semantic web. WWW2011: 87-96.
  16. Tang J, Fong A C M, Wang B, et al. A unified probabilistic framework for name disambiguation in digital library. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(6): 975-987.
  17. Zhang Y, Zhang F, Yao P, et al. Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. KDD2018: 1002-1011.
  18. Ngomo A C N, Auer S. LIMES—a time-efficient approach for large-scale link discovery on the web of data. IJCAI2011.

知识图谱嵌入

  • ---Review---
  1. Wang Q, Mao Z, Wang B, et al. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12): 2724-2743.
  2. 刘知远, 孙茂松, 林衍凯, 等. 知识表示学习研究进展. 计算机研究与发展, 2016, 53(2): 247-261.
  • ---Basic Models---
  1. Turian J, Ratinov L, Bengio Y. Word representations: A simple and general method for semi-supervised learning. Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics, 2010: 384-394. (one-hot)
  2. Bordes A, Glorot X, Weston J, et al. Joint learning of words and meaning representations for open-text semantic parsing. Artificial Intelligence and Statistics. 2012: 127-135. (UM)
  3. Bordes A, Weston J, Collobert R, et al. Learning structured embeddings of knowledge bases. AAAI. 2011. (SE)
  4. Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. NIPS2013: 3111-3119.
  • ---Translation-based Models(Basic Models)---
  1. Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data. NIPS2013: 2787-2795.(TransE)
  2. Wang Z, Zhang J, Feng J, et al. Knowledge graph embedding by translating on hyperplanes. AAAI2014.(TransH)
  3. Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion. AAAI2015.(TransR/CTransR)
  4. Ji G, He S, Xu L, et al. Knowledge graph embedding via dynamic mapping matrix. ACL2015: 687-696. (TransD)
  5. Ji G, Liu K, He S, et al. Knowledge graph completion with adaptive sparse transfer matrix. AAAI. 2016. (TansSparse)
  • ---Translation-based Models(Translation Requirements Relaxing)---
  1. Fan M, Zhou Q, Chang E, et al. Transition-based knowledge graph embedding with relational mapping properties. Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing. 2014. (TransM)
  2. Xiao H, Huang M, Zhu X. From one point to a manifold: Knowledge graph embedding for precise link prediction. arXiv preprint arXiv:1512.04792, 2015. (ManifoldE)
  3. Feng J, Huang M, Wang M, et al. Knowledge graph embedding by flexible translation. Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning. 2016. (TransF)
  4. Xiao H, Huang M, Hao Y, et al. TransA: An adaptive approach for knowledge graph embedding. arXiv preprint arXiv:1509.05490, 2015. (TransA)
  • ---Translation-based Models(Gaussian Distribution Models)---
  1. He S, Liu K, Ji G, et al. Learning to represent knowledge graphs with gaussian embedding. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 623-632. (KB2E)
  2. Xiao H, Huang M, Zhu X. TransG: A generative model for knowledge graph embedding. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1: 2316-2325. （TransG）
  • ---Semantic Matching Models(Matrix Factorization Models)---
  1. Jenatton R, Roux N L, Bordes A, et al. A latent factor model for highly multi-relational data. NIPS. 2012: 3167-3175. (LFM)
  2. Nickel M, Tresp V, Kriegel H P. A Three-Way Model for Collective Learning on Multi-Relational Data. ICML. 2011, 11: 809-816. (RESCAL)
  3. Yang B, Yih W, He X, et al. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575, 2014. (DistMult)
  4. Nickel M, Rosasco L, Poggio T. Holographic embeddings of knowledge graphs. AAAI. 2016. (HolE)
  5. Trouillon T, Welbl J, Riedel S, et al. Complex embeddings for simple link prediction. International Conference on Machine Learning. 2016: 2071-2080. (ComplEx)
  6. Liu H, Wu Y, Yang Y. Analogical inference for multi-relational embeddings. Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017: 2168-2178. (ANALOGY)
  • ---Semantic Matching Models(Neural Network Models)---
  1. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (SLM)
  2. Bordes A, Glorot X, Weston J, et al. A semantic matching energy function for learning with multi-relational data. Machine Learning, 2014, 94(2): 233-259. (SME)
  3. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (NTN)
  4. Dong X, Gabrilovich E, Heitz G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014: 601-610. (MLP)
  5. Liu Q, Jiang H, Evdokimov A, et al. Probabilistic reasoning via deep learning: Neural association models. arXiv preprint arXiv:1603.07704, 2016. (NAM)
  6. Dettmers T, Minervini P, Stenetorp P, et al. Convolutional 2d knowledge graph embeddings. AAAI. 2018. (ConvE)
  • ---Multi-source Information Fusion Models(Entity Type)---
  1. Guo S, Wang Q, Wang B, et al. Semantically smooth knowledge graph embedding. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015, 1: 84-94. (SSE)
  2. Xie R, Liu Z, Sun M. Representation Learning of Knowledge Graphs with Hierarchical Types. IJCAI. 2016: 2965-2971. (TKRL)
  • ---Multi-source Information Fusion Models(Relation Paths)---
  1. Lin Y, Liu Z, Luan H, et al. Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379, 2015. (PTransE)
  2. Dong X, Gabrilovich E, Heitz G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014: 601-610. (MLP+PRA)
  3. Nickel M, Jiang X, Tresp V. Reducing the rank in relational factorization models by including observable patterns. NIPS. 2014: 1179-1187. (PRA+RESCAL)
  • ---Multi-source Information Fusion Models(Textual Descriptions)---
  1. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (NTN)
  2. Xie R, Liu Z, Jia J, et al. Representation learning of knowledge graphs with entity descriptions. AAAI. 2016. (DKRL)
  3. Xiao H, Huang M, Meng L, et al. SSP: semantic space projection for knowledge graph embedding with text descriptions. AAAI. 2017. (SSP)
  4. Wang Z, Li J Z. Text-Enhanced Representation Learning for Knowledge Graph. IJCAI. 2016: 1293-1299. (TEKE)
  5. Wang Z, Zhang J, Feng J, et al. Knowledge graph and text jointly embedding. EMNLP. 2014: 1591-1601.
  • ---Multi-source Information Fusion Models(Logical Rules)---
  1. Wang Q, Wang B, Guo L. Knowledge base completion using embeddings and rules. IJCAI. 2015.
  2. Guo S, Wang Q, Wang L, et al. Jointly embedding knowledge graphs and logical rules. EMNLP. 2016: 192-202. (KALE)
  3. Guo S, Wang Q, Wang L, et al. Knowledge graph embedding with iterative guidance from soft rules. AAAI. 2018. (RUGE)
  4. Ding B, Wang Q, Wang B, et al. Improving knowledge graph embedding using simple constraints. arXiv preprint arXiv:1805.02408, 2018.
  • ---Multi-source Information Fusion Models(Entity Attributes)---
  1. Nickel M, Tresp V, Kriegel H P. Factorizing yago: scalable machine learning for linked data. Proceedings of the 21st international conference on World Wide Web. ACM, 2012: 271-280.
  • ---Multi-source Information Fusion Models(Temporal Information)---
  1. Jiang T, Liu T, Ge T, et al. Encoding temporal information for time-aware link prediction. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 2350-2354.
  • ---Multi-source Information Fusion Models(Graph Structure)---
  1. Feng J, Huang M, Yang Y. GAKE: graph aware knowledge embedding. COLING. 2016: 641-651. (GAKE)

知识推理/知识挖掘

  1. Nickel M, Tresp V, Kriegel H P. A Three-Way Model for Collective Learning on Multi-Relational Data. ICML2011: 809-816.
  2. Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS2013: 926-934.
  3. Lao N, Cohen W W. Relational retrieval using a combination of path-constrained random walks. Machine learning, 2010, 81(1): 53-67.
  4. Lin Y, Liu Z, Luan H, et al. Modeling relation paths for representation learning of knowledge bases. EMNLP2015.
  5. Gardner M, Talukdar P, Krishnamurthy J, et al. Incorporating vector space similarity in random walk inference over knowledge bases. EMNLP2014: 397-406.
  6. Xiong W, Hoang T, Wang W Y. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning. EMNLP2017:564-573.

知识存储/知识查询

  1. Bornea M A, Dolby J, Kementsietsidis A, et al. Building an efficient RDF store over a relational database. SIGMOD2013: 121-132.
  2. Huang J, Abadi D J, Ren K. Scalable SPARQL querying of large RDF graphs. Proceedings of the VLDB Endowment, 2011, 4(11): 1123-1134.
  3. Zou L, Özsu M T, Chen L, et al. gStore: a graph-based SPARQL query engine. The VLDB Journal—The International Journal on Very Large Data Bases, 2014, 23(4): 565-590.

人机交互

  1. Ferrucci D A. Introduction to “this is watson”. IBM Journal of Research and Development, 2012, 56(3.4): 1: 1-1: 15.
  2. Lally A, Prager J M, McCord M C, et al. Question analysis: How Watson reads a clue. IBM Journal of Research and Development, 2012, 56(3.4): 2: 1-2: 14.
  3. Zhou H, Young T, Huang M, et al. Commonsense Knowledge Aware Conversation Generation with Graph Attention. IJCAI. 2018: 4623-4629.
  4. Zhu Y, Zhang C, Ré C, et al. Building a large-scale multimodal knowledge base system for answering visual queries. arXiv:1507.05670, 2015.
  5. Auli M, Galley M, Quirk C, et al. Joint language and translation modeling with recurrent neural networks. EMNLP2013:1044–1054.
  6. Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.
  7. Cho K, Van Merriënboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. EMNLP2014.
  8. Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014.
  9. Graves A. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.

附录B：最新进展论文选读(近1年内)

  1. Bhatia S, Dwivedi P, Kaur A. That’s Interesting, Tell Me More! Finding Descriptive Support Passages for Knowledge Graph Relationships. ISWC2018: 250-267. (Best Paper)
  2. Soulet A, Giacometti A, Markhoff B, et al. Representativeness of Knowledge Bases with the Generalized Benford’s Law. ISWC2018: 374-390.
  3. Wang M, Wang R, Liu J, et al. Towards Empty Answers in SPARQL: Approximating Querying with RDF Embedding. ISWC2018: 513-529.
  4. Salas J, Hogan A. Canonicalisation of monotone SPARQL queries. ISWC2018: 600-616. (Best Student Paper)
  5. Pertsas V, Constantopoulos P, Androutsopoulos I. Ontology Driven Extraction of Research Processes. ISWC2018:162-178.
  6. Saeedi A, Peukert E, Rahm E. Using link features for entity clustering in knowledge graphs. ESWC2018: 576-592. (Best Paper)
  7. Schlichtkrull M, Kipf T N, Bloem P, et al. Modeling relational data with graph convolutional networks. ESWC2018: 593-607. (Best Student Paper)
  8. Hamid Z, Giulio N, Jens L. Formal Query Generation for Question Answering over Knowledge Bases. ESWC2018:714-728.
  9. Zhou L, Gao J, Li D, et al. The Design and Implementation of XiaoIce, an Empathetic Social Chatbot. arXiv preprint arXiv:1812.08989, 2018.
  10. Dasgupta S S, Ray S N, Talukdar P. HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. EMNLP2018: 2001-2011.
  11. Dubey M, Banerjee D, Chaudhuri D, et al. EARL: Joint entity and relation linking for question answering over knowledge graphsISWC2018: 108-126.
  12. Chen M, Tian Y, Chang K W, et al. Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignment. IJCAI2018.
  13. Janke D, Staab S, Thimm M. Impact analysis of data placement strategies on query efforts in distributed rdf stores. Journal of Web Semantics, 2018, 50: 21-48.
  14. Han X, Zhu H, Yu P, et al. FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation. EMNLP2018.
  15. Hou Y, Liu Y, Che W, et al. Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding. ACL2018: 1234-1245.
  16. Tran V K, Nguyen L M. Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems. COLING2018: 1205-1217.
  17. Zhang W, Cui Y, Wang Y, et al. Context-Sensitive Generation of Open-Domain Conversational Responses. COLING2018: 2437-2447.
  18. Shi W, Yu Z. Sentiment Adaptive End-to-End Dialog Systems. ACL2018, 1: 1509-1519.
  19. Zhang S, Dinan E, Urbanek J, et al. Personalizing Dialogue Agents: I have a dog, do you have pets too? ACL2018, 1: 2204-2213.
  20. Wei Z, Liu Q, Peng B, et al. Task-oriented dialogue system for automatic diagnosis. ACL2018, 2: 201-207.
  21. Sungjoon Park, Donghyun Kim and Alice Oh. Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues. NAACL2019.
  22. Sebastian R. Neural Transfer Learning for Natural Language Processing. PhD Thesis. National University of Ireland, 2019.
  • ---实体识别(ACL)---
  1. Parvez M R, Chakraborty S, Ray B, et al. Building language models for text with named entities. arXiv preprint arXiv:1805.04836, 2018.
  2. Lin Y, Yang S, Stoyanov V, et al. A multi-lingual multi-task architecture for low-resource sequence labeling. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, 1: 799-809.
  3. Xu H, Liu B, Shu L, et al. Double embeddings and cnn-based sequence labeling for aspect extraction. arXiv preprint arXiv:1805.04601, 2018.
  4. Ye Z X, Ling Z H. Hybrid semi-markov crf for neural sequence labeling. arXiv preprint arXiv:1805.03838, 2018.
  5. Yang J, Zhang Y. Ncrf++: An open-source neural sequence labeling toolkit. arXiv preprint arXiv:1806.05626, 2018.
  • ---实体识别(NAACL)---
  1. Ju M, Miwa M, Ananiadou S. A neural layered model for nested named entity recognition. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018, 1: 1446-1459.
  2. Wang Z, Qu Y, Chen L, et al. Label-aware double transfer learning for cross-specialty medical named entity recognition. NAACL2018.
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  • ---实体识别(EMNLP)---
  1. Cao P, Chen Y, Liu K, et al. Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism.EMNLP2018: 182-192.
  2. Xie J, Yang Z, Neubig G, et al. Neural cross-lingual named entity recognition with minimal resources. EMNLP2018.
  3. Lin B Y, Lu W. Neural adaptation layers for cross-domain named entity recognition. EMNLP2018.
  4. Shang J, Liu L, Ren X, et al. Learning Named Entity Tagger using Domain-Specific Dictionary. EMNLP2018.
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  • ---实体识别(COLING)---
  1. Mai K, Pham T H, Nguyen M T, et al. An empirical study on fine-grained named entity recognition. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 711-722.
  2. Nagesh A, Surdeanu M. An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 2312-2324.
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  • ---事件抽取(ACL)---
  1. Choubey P K, Huang R. Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures.Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, 1: 485-495.
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  5. Zhao Y, Jin X, Wang Y, et al. Document embedding enhanced event detection with hierarchical and supervised attention. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2018, 2: 414-419.
  6. Yang H, Chen Y, Liu K, et al. DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data. ACL2018, System Demonstrations, 2018: 50-55.
  • ---事件抽取(NAACL)---
  1. Ferguson J, Lockard C, Weld D S, et al. Semi-Supervised Event Extraction with Paraphrase Clusters. ACL2018.
  • ---事件抽取(EMNLP)---
  1. Orr J W, Tadepalli P, Fern X. Event Detection with Neural Networks: A Rigorous Empirical Evaluation. EMNLP2018.
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  5. Lu W, Nguyen T H. Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 4822-4828.
  • ---事件抽取(COLING)---
  1. Araki J, Mitamura T. Open-Domain Event Detection using Distant Supervision. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 878-891.
  2. Muis A O, Otani N, Vyas N, et al. Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 70-82.
  3. Kazeminejad G, Bonial C, Brown S W, et al. Automatically Extracting Qualia Relations for the Rich Event Ontology. Proceedings of the 27th International Conference on Computational Linguistics. 2018: 2644-2652.
  4. Liu Z, Mitamura T, Hovy E. Graph-Based Decoding for Event Sequencing and Coreference Resolution. COLING2018.