Embeddings in Natural Language Processing - (Synthesis Lectures on Human Language Technologies) by Mohammad Taher Pilehvar & Jose Camacho-Collados
Similar Products
Products of same category from the store
All
Product info
<p/><br></br><p><b> Book Synopsis </b></p></br></br><p><b>Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP).</b> 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.</p> <p>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.</p> <p>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.</p><p/><br></br><p><b> About the Author </b></p></br></br><b>Mohammad Taher Pilehvar</b> is an Assistant Professor at the Tehran Institute for Advanced Studies (TeIAS) and an Affiliated Lecturer at the University of Cambridge. Taher's research is primarily in Lexical Semantics with a special focus on representation learning for word senses. Taher has co-instructed multiple tutorials at *ACL conferences and co-organized four SemEval tasks and an EACL workshop on semantic representation. Taher has contributed to the field of lexical semantics with several publications in the recent years, including two best paper nominees at ACL (2013 and 2017) and a survey on vector representations of meaning.
Price History
Price Archive shows prices from various stores, lets you see history and find the cheapest. There is no actual sale on the website. For all support, inquiry and suggestion messagescommunication@pricearchive.us