Embeddings in Natural Language Processing : Theory and Advances in Vector Representations of Meaning
Pilehvar, Mohammad Taher
Embeddings in Natural Language Processing : Theory and Advances in Vector Representations of Meaning by Mohammad Taher Pilehvar, Jose Camacho-Collados - 1st edition 2021 - 1 recurso en línea (XVIII, 157 páginas) - Synthesis Lectures on Human Language Technologies 1947-4059 .
Preface -- Introduction -- Background -- Word Embeddings -- Graph Embeddings -- Sense Embeddings -- Contextualized Embeddings -- Sentence and Document Embeddings -- Ethics and Bias -- Conclusions -- Bibliography -- Authors' Biographies.
Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.
9783031021770
10.1007/978-3-031-02177-0 doi
Proceso en lenguaje natural (Informática)
Lenguajes de programación--Semántica
Inteligencia artificial
QA76.9.N38 / 2021 EB
Embeddings in Natural Language Processing : Theory and Advances in Vector Representations of Meaning by Mohammad Taher Pilehvar, Jose Camacho-Collados - 1st edition 2021 - 1 recurso en línea (XVIII, 157 páginas) - Synthesis Lectures on Human Language Technologies 1947-4059 .
Preface -- Introduction -- Background -- Word Embeddings -- Graph Embeddings -- Sense Embeddings -- Contextualized Embeddings -- Sentence and Document Embeddings -- Ethics and Bias -- Conclusions -- Bibliography -- Authors' Biographies.
Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.
9783031021770
10.1007/978-3-031-02177-0 doi
Proceso en lenguaje natural (Informática)
Lenguajes de programación--Semántica
Inteligencia artificial
QA76.9.N38 / 2021 EB