Embeddings in Natural Language Processing (Record no. 85019)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03574nam a22005415i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-02177-0 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163829.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2021 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031021770 |
| -- | 978-3-031-02177-0 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Pilehvar, Mohammad Taher. |
| 245 10 - TITLE STATEMENT | |
| Title | Embeddings in Natural Language Processing |
| Sub Title | Theory and Advances in Vector Representations of Meaning / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XVIII, 157 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Human Language Technologies, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Preface -- Introduction -- Background -- Word Embeddings -- Graph Embeddings -- Sense Embeddings -- Contextualized Embeddings -- Sentence and Document Embeddings -- Ethics and Bias -- Conclusions -- Bibliography -- Authors' Biographies. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Camacho-Collados, Jose. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-02177-0 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2021. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural language processing (Computer science). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational linguistics. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural Language Processing (NLP). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Linguistics. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1947-4059 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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