Yang Liu

Maosong Sun

Zhiyuan Liu

Ting Liu

Xiaojie Wang

This book constitutes the proceedings of the 17th China National Conference on Computational Linguistics, CCL 2018, and the 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018, held in Changsha, China, in October 2018. The 33 full papers presented in this volume were carefully reviewed and selected from 84 submissions. They are organized in topical sections named: Semantics; machine translation; knowledge graph and information extraction; linguistic resource annotation and evaluation; information retrieval and question answering; text classification and summarization; social computing and sentiment analysis; and NLP applications.

17th China National Conference, CCL 2018, and 6th International Symposium, NLP-NABD 2018, Changsha, China, October 19–21, 2018, Proceedings

Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Baobao Chang

Deyi Xiong

This book constitutes the proceedings of the 16th China National Conference on Computational Linguistics, CCL 2017, and the 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017, held in Nanjing, China, in October 2017. The 39 full papers presented in this volume were carefully reviewed and selected from 272 submissions. They were organized in topical sections named: Fundamental theory and methods of computational linguistics; Machine translation and multilingual information processing; Knowledge graph and information extraction; Language resource and evaluation; Information retrieval and question answering; Text classification and summarization; Social computing and sentiment analysis; NLP applications; Minority language information processing.

16th China National Conference, CCL 2017, and 5th International Symposium, NLP-NABD 2017, Nanjing, China, October 13-15, 2017, Proceedings

Min Zhang

This book constitutes the refereed proceedings of the 14th China National Conference on Computational Linguistics, CCL 2014, and of the Third International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2015, held in Guangzhou, China, in November 2015. The 34 papers presented were carefully reviewed and selected from 283 submissions. The papers are organized in topical sections on lexical semantics and ontologies; semantics; sentiment analysis, opinion mining and text classification; machine translation; multilinguality in NLP; machine learning methods for NLP; knowledge graph and information extraction; discourse, coreference and pragmatics; information retrieval and question answering; social computing; NLP applications.

14th China National Conference, CCL 2015 and Third International Symposium, NLP-NABD 2015, Guangzhou, China, November 13-14, 2015, Proceedings

Hongfei Lin

Xuanjing Huang

This book constitutes the proceedings of the 15th China National Conference on Computational Linguistics, CCL 2016, and the 4th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016, held in Yantai City, China, in October 2016. The 29 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 85 submissions. They were organized in topical sections named: semantics; machine translation; multilinguality in NLP; knowledge graph and information extraction; linguistic resource annotation and evaluation; information retrieval and question answering; text classification and summarization; social computing and sentiment analysis; and NLP applications.

15th China National Conference, CCL 2016, and 4th International Symposium, NLP-NABD 2016, Yantai, China, October 15-16, 2016, Proceedings

Cheng Yang

Cunchao Tu

Chuan Shi

Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction. This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.

Theories, Methods, and Applications

Network Embedding

19th China National Conference, CCL 2020, Hainan, China, October 30 – November 1, 2020, Proceedings

Chinese Computational Linguistics

Yankai Lin

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

Representation Learning for Natural Language Processing

Maosong Sun

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