Name: Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning (en Inglés)
Price: 64760.00 CLP
Availability: InStock
Author: Pilehvar, Mohammad Taher ; Camacho-Collados, Jose
ISBN: 9783031010491

portada Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning (en Inglés)

Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning (en Inglés)

Formato

Libro Físico

Autor

Mohammad Taher Pilehvar

Jose Camacho-Collados

Editorial

Springer

Idioma

Inglés

N° páginas

157

Encuadernación

Tapa Blanda

Dimensiones

23.5 x 19.1 x 1.0 cm

Peso

0.31 kg.

ISBN13

9783031010491

Categorías

Semántica, análisis del discurso, etc.
Lenguaje natural y traducción automática
Reconocimiento del habla
Procesamiento del sonido

Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning (en Inglés)

Mohammad Taher Pilehvar (Autor) · Jose Camacho-Collados (Autor) · Springer · Tapa Blanda

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Reseña del libro "Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning (en Inglés)"

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.