Theory and Advances in Vector Representations of Meaning
Mohammad Taher Pilehvar, Tehran Institute for Advanced Studies
Jose Camacho-Collados, Cardiff University
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. A high-level synthesis of the main embedding techniques in natural language processing including an overview of recent developments in contextualized representations.
• Word Embeddings
• Graph Embeddings
• Sense Embeddings
• Contextualized Embeddings
• Sentence and Document Embeddings
• Ethics and Bias
• Authors’ Biographies
An earlier draft of the book can be obtained from here.
Print and e-books of the final version.