Natural Language Processing and Chinese Computing: 11th CCF International Conference, NLPCC 2022, Guilin, China, September 24-25, 2022, Proceedings, Part II

The 11th CCF Conference on Natural Language Processing and Chinese Computing,NLPCC 2022, held in Guilin, China, had 327 submissions, with 62 full papers, 21 poster papers, and 27 workshop papers accepted. They cover Fundamentals of NLP, Machine Translation and Multilinguality, Machine Learning for NLP, Information Extraction and Knowledge Graph, Summarization and Generation, Question Answering, Dialogue Systems, Social Media and Sentiment Analysis, NLP Applications and Text Mining, and Multimodality and Explainability.

Format: Paperback / softback
Length: 363 pages
Publication date: 25 September 2022
Publisher: Springer International Publishing AG

This two-volume set, comprising LNAI 13551 and 13552, serves as the official publication of the 11th CCF Conference on Natural Language Processing and Chinese Computing (NLPCC 2022), which took place in Guilin, China, in September 2022. The conference featured a remarkable array of 62 full papers, 21 poster papers, and 27 workshop papers, all of which underwent rigorous review and selection from a total of 327 submissions. These papers are thoughtfully organized into the following topical areas:

Fundamentals of Natural Language Processing: This section encompasses fundamental concepts, theories, and methodologies in the field of natural language processing. It explores topics such as language representation, parsing, semantics, and machine translation.

Machine Translation and Multilinguality: This area focuses on the challenges and advancements in machine translation, including bilingual and multilingual translation systems, translation quality assessment, and cross-lingual information retrieval. It also explores the role of multilingualism in natural language processing and its applications in various domains.

Machine Learning for Natural Language Processing: This section explores the use of machine learning techniques in natural language processing, including supervised learning, unsupervised learning, and deep learning. It covers topics such as text classification, sentiment analysis, named entity recognition, and language modeling.

Information Extraction and Knowledge Graph: This area explores the extraction of information from text, the construction of knowledge graphs, and their applications in various domains such as information retrieval, knowledge management, and machine learning. It also discusses the use of graph neural networks and other deep learning models for knowledge representation.

Summarization and Generation: This section focuses on the techniques and algorithms for summarizing and generating text. It covers topics such as text summarization, text generation, and dialogue systems. It also explores the use of transformer-based models for language generation.

Question Answering: This area explores the techniques and algorithms for answering questions from text. It covers topics such as question answering systems, knowledge base construction, and natural language inference. It also discusses the use of deep learning models for question answering.

Dialogue Systems: This section explores the design and development of dialogue systems, including chatbots, virtual assistants, and human-computer interfaces. It covers topics such as dialogue modeling, dialogue generation, and user behavior analysis.

Social Media and Sentiment Analysis: This area focuses on the analysis of social media data and sentiment analysis. It covers topics such as social media mining, sentiment analysis, emotion recognition, and opinion mining. It also explores the use of natural language processing techniques for understanding social media discourse.

NLP Applications and Text Mining: This section explores the applications of natural language processing in various domains, including healthcare, finance, education, and law. It covers topics such as medical text analysis, financial text analysis, text mining for education, and legal text analysis.

Multimodality and Explainability: This area explores the use of multimodal data and explainability techniques in natural language processing. It covers topics such as multimodal sentiment analysis, visual language processing, and interpretable machine learning.

In conclusion, this two-volume set of LNAI 13551 and 13552 represents a comprehensive collection of cutting-edge research and developments in natural language processing and Chinese computing. The contributions of the authors and presenters at NLPCC 2022 have significantly advanced our understanding of these fields and paved the way for future advancements. The papers presented cover a wide range of topics and provide valuable insights into the latest trends, techniques, and applications in natural language processing and Chinese computing.

Weight: 605g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031171888
Edition number: 1st ed. 2022