Computational Linguistics and Intelligent Text Processing: 20th International Conference, CICLing 2019, La Rochelle, France, April 7-13, 2019, Revised Selected Papers, Part II

LNCS 13451 and 13452 are two-volume sets that contain revised selected papers from the CICLing 2019 conference, with a total of 95 papers and 3 invited papers organized in various topical sections.

Format: Paperback / softback
Length: 674 pages
Publication date: 26 February 2023
Publisher: Springer International Publishing AG

The two-volume set LNCS 13451 and 13452, comprising revised selected papers from the CICLing 2019 conference, held in La Rochelle, France, in April 2019, is now available. This comprehensive collection showcases a remarkable 95 papers, meticulously reviewed and chosen from a pool of 335 submissions. Additionally, the book features 3 invited papers, adding even more depth and expertise to its content.

The papers in this set are thoughtfully organized into the following topical sections:

General: This section encompasses a wide range of topics related to natural language processing (NLP). It includes papers that explore fundamental concepts, algorithms, and applications in various NLP domains.

Information Extraction: This section focuses on extracting relevant information from text, such as identifying entities, relations, and patterns. It covers techniques such as named entity recognition, entity linking, and information extraction from biomedical texts.

Information Retrieval: This section explores the process of retrieving information from large databases and information systems. It covers topics such as query formulation, ranking, and relevance feedback, as well as text mining and information retrieval algorithms.

Language Modeling: This section explores language modeling techniques, including statistical and deep learning models, for generating natural language text. It covers topics such as word embeddings, recurrent neural networks, and generative adversarial networks.

Lexical Resources: This section focuses on the development and utilization of lexical resources, such as dictionaries, thesauri, and corpora. It covers topics such as lexical resource creation, annotation, and usage in NLP applications.

Machine Translation: This section explores machine translation algorithms and systems, including statistical, neural, and hybrid approaches. It covers topics such as translation quality assessment, machine translation evaluation, and bilingual lexicon development.

Morphology: This section explores the study of word morphology, including morphology analysis, stemming, and lemmatization. It covers topics such as part-of-speech tagging, word sense disambiguation, and morphological processing in natural language processing.

Syntax and Parsing: This section focuses on the analysis and processing of syntactic structures in natural language text. It covers topics such as sentence parsing, dependency parsing, and semantic parsing, as well as parsing algorithms and models.

Name Entity Recognition: This section explores the task of identifying and categorizing named entities in text, such as persons, organizations, and locations. It covers techniques such as named entity recognition, entity linking, and entity disambiguation.

Semantics and Text Similarity: This section explores the relationship between semantics and text similarity. It covers topics such as semantic similarity measures, text similarity algorithms, and applications in information retrieval and text mining.

Sentiment Analysis: This section focuses on the analysis of sentiment and emotion in natural language text. It covers topics such as sentiment classification, sentiment analysis algorithms, and applications in social media analysis and customer feedback analysis.

Speech Processing: This section explores the processing of spoken language, including speech recognition, speech synthesis, and speech understanding. It covers topics such as speech signal processing, speech recognition algorithms, and speech synthesis models.

Text Categorization: This section explores the task of categorizing text into specific topics or genres. It covers topics such as text classification, topic modeling, and text clustering algorithms.

Text Generation: This section explores the generation of natural language text, including text summarization, text generation algorithms, and applications in chatbots and virtual assistants.

Text Mining: This section explores the extraction and analysis of patterns and trends from large collections of text. It covers topics such as text mining algorithms, data mining, and knowledge discovery in text.

In conclusion, the two-volume set LNCS 13451 and 13452 serves as a valuable resource for researchers, practitioners, and students in the field of natural language processing. It provides a comprehensive overview of the latest advancements and research in NLP, covering a wide range of topics and applications. The carefully selected papers and invited contributions make this set an essential reference for anyone interested in advancing their knowledge and expertise in this dynamic field.

Weight: 1050g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031243394
Edition number: 1st ed. 2023