Embeddings in Natural Language Processing, Pilehvar, Mohammad Taher Camacho-Collados, Jose
Автор: Maosong Sun, Xiaojie Wang, Baobao Chang Название: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data ISBN: 3319690043 ISBN-13(EAN): 9783319690049 Издательство: Springer Рейтинг: Цена: 5300.00 р. Наличие на складе: Есть (3 шт.) Описание: 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. Minority language information processing.
Автор: Joseph Olive, Caitlin Christianson, John McCary Название: Handbook of Natural Language Processing and Machine Translation ISBN: 1441977120 ISBN-13(EAN): 9781441977120 Издательство: Springer Рейтинг: Цена: 34937.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This comprehensive handbook, written by leading experts in the field, details the groundbreaking research conducted under the breakthrough GALE program--The Global Autonomous Language Exploitation within the Defense Advanced Research Projects Agency (DARPA), while placing it in the context of previous research in the fields of natural language and signal processing, artificial intelligence and machine translation.The most fundamental contrast between GALE and its predecessor programs was its holistic integration of previously separate or sequential processes. In earlier language research programs, each of the individual processes was performed separately and sequentially: speech recognition, language recognition, transcription, translation, and content summarization. The GALE program employed a distinctly new approach by executing these processes simultaneously. Speech and language recognition algorithms now aid translation and transcription processes and vice versa. This combination of previously distinct processes has produced significant research and performance breakthroughs and has fundamentally changed the natural language processing and machine translation fields.This comprehensive handbook provides an exhaustive exploration into these latest technologies in natural language, speech and signal processing, and machine translation, providing researchers, practitioners and students with an authoritative reference on the topic.
Автор: Rieser, Verena Название: Reinforcement Learning for Adaptive Dialogue Systems ISBN: 3642249418 ISBN-13(EAN): 9783642249419 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The past decade has seen a revolution in the field of spoken dialogue systems. As in other areas of Computer Science and Artificial Intelligence, data-driven methods are now being used to drive new methodologies for system development and evaluation. This book is a unique contribution to that ongoing change. A new methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies. The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development – not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general.
Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao Название: Network Embedding: Theories, Methods, and Applications ISBN: 1636390463 ISBN-13(EAN): 9781636390468 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 15939.00 р. Наличие на складе: Нет в наличии.
Описание:
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.
Автор: Yun Fu; Yunqian Ma Название: Graph Embedding for Pattern Analysis ISBN: 1489990623 ISBN-13(EAN): 9781489990624 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph and graph in vector spaces, and describes their real-world applications.
Автор: Yang, Cheng Liu, Zhiyuan Tu, Cunchao Shi, Chuan Sun, Maosong Название: Network Embedding ISBN: 3031004620 ISBN-13(EAN): 9783031004629 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao Название: Network Embedding: Theories, Methods, and Applications ISBN: 1636390447 ISBN-13(EAN): 9781636390444 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12751.00 р. Наличие на складе: Нет в наличии.
Описание:
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.
Автор: Muandet, Krikamol Fukumizu, Kenji Sriperumbudur, Bharath Scholkopf, Bernhard Название: Kernel mean embedding of distributions: ISBN: 1680832883 ISBN-13(EAN): 9781680832884 Издательство: Неизвестно Рейтинг: Цена: 13656.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This monograph provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions. The targeted audience includes graduate students and researchers in machine learning and statistics who are interested in the theory and applications of kernel mean embeddings.
Описание: A book for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics.
Описание: This book constitutes the refereed proceedings of the 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021, held online in July 2021. The 19 full papers and 14 short papers were carefully reviewed and selected from 82 submissions.
Описание: Posters - Fundamentals of NLP.- Syntax and Coherence - The Effect on Automatic Argument Quality Assessment.- ExperienceGen 1.0: A Text Generation Challenge Which Requires Deduction and Induction Ability.- Machine Translation and Multilinguality.- SynXLM-R: Syntax-enhanced XLM-R in Translation Quality Estimation.- Machine Learning for NLP.- Memetic Federated Learning for Biomedical Natural Language Processing.- Information Extraction and Knowledge Graph.- Event Argument Extraction via a Distance-Sensitive Graph Convolutional Network.- Exploit Vague Relation: An Augmented Temporal Relation Corpus and Evaluation.- Searching Effective Transformer for Seq2Seq Keyphrase Generation.- Prerequisite Learning with Pre-trained Language and Graph Embedding Models.- Summarization and Generation.- Variational Autoencoder with Interactive Attention for Affective Text Generation.- CUSTOM: Aspect-Oriented Product Summarization for E-Commerce.- Question Answering.- FABERT: A Feature Aggregation BERT-Based Model for Document Reranking.- Generating Relevant, Correct and Fluent Answers in Natural Answer Generation.- GeoCQA: A Large-scale Geography-Domain Chinese Question Answering Dataset from Examination.- Dialogue Systems.- Generating Informative Dialogue Responses with Keywords-Guided Networks.- Zero-Shot Deployment for Cross-Lingual Dialogue System.- MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation.- EmoDialoGPT: Enhancing DialoGPT with Emotion.- Social Media and Sentiment Analysis.- BERT-based Meta-learning Approach with Looking Back for Sentiment Analysis of Literary Book Reviews.- ISWR: an Implicit Sentiment Words Recognition Model Based on Sentiment Propagation.- An Aspect-Centralized Graph Convolutional Network for Aspect-based Sentiment Classification.- NLP Applications and Text Mining.- Capturing Global Informativeness in Open Domain Keyphrase Extraction.- Background Semantic Information Improves VerbalMetaphor Identification.- Multimodality and Explainability.- Towards unifying the explainability evaluation methods for NLP.- Explainable AI Workshop.- Detecting Covariate Drift with Explanations.- A Data-Centric Approach Towards Deducing Bias in Artificial Intelligence Systems for Textual Contexts.- Student Workshop.- Enhancing Model Robustness via Lexical Distilling.- Multi-stage Multi-modal Pre-training for Video Representation.- Nested Causality Extraction on Traffic Accident Texts as Question Answering.- Evaluation Workshop.- MSDF: A General Open-Domain Multi-Skill Dialog Framework.- RoKGDS: A Robust Knowledge Grounded Dialog System.- Enhanced Few-shot Learning with Multiple-Pattern-Exploiting Training.- BIT-Event at NLPCC-2021 Task 3: Subevent Identification via Adversarial Training.- Few-shot Learning for Chinese NLP tasks.- When Few-shot Learning Meets Large-scale Knowledge-enhanced Pre-training: Alibaba at FewCLUE.- TKB ert: Two-stage Knowledge Infused Behavioral Fine-tuned BERT.- A Unified Information Extraction System Based on Role Recognition and Combination.- A Simple but Effective System for Multi-format Information Extraction.- A Hierarchical Sequence Labeling Model for Argument Pair Extraction.- Distant finetuning with discourse relations for stance classification.- The Solution of Xiaomi AI Lab to the 2021 Language and Intelligence Challenge: Multi-Format Information Extraction Task.- A Unified Platform for Information Extraction with Two-stage Process.- Overview of the NLPCC 2021 Shared Task: AutoIE2.- Task 1 - Argumentative Text Understanding for AI Debater (AIDebater).- Two Stage Learning for Argument Pairs Extraction.- Overview of Argumentative Text Understanding for AI Debater Challenge.- ACE: A Context-Enhanced model for Interactive Argument Pair Identification.- Context-Aware and Data-Augmented Transformer for Interactive Argument Pai
Автор: Carlos Peri??n-Pascual, Eva M. Mestre-Mestre Название: Understanding Meaning and Knowledge Representation ISBN: 1443884618 ISBN-13(EAN): 9781443884617 Издательство: Cambridge Scholars Рейтинг: Цена: 6715.00 р. Наличие на складе: Нет в наличии.
Описание: Today, there is a need to develop natural language processing (NLP) systems from deeper linguistic approaches. Although there are many NLP applications which can work without taking into account any linguistic theory, this type of system can only be described as “deceptively intelligent”. On the other hand, however, those computer programs requiring some language comprehension capability should be grounded in a robust linguistic model if they are to display the expected behaviour. The purpose of this book is to examine and discuss recent work in meaning and knowledge representation within theoretical linguistics and cognitive linguistics, particularly research which can be reused to model NLP applications.
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