Cross Lingual Word Embeddings For Low Resource And Morphologically Rich Languages
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| Author | : Anders Søgaard |
| Publisher | : Springer Nature |
| Total Pages | : 120 |
| Release | : 2022-05-31 |
| Genre | : Computers |
| ISBN | : 3031021711 |
The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano--and most other languages--remains limited. Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages. In this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The survey is intended to be systematic, using consistent notation and putting the available methods on comparable form, making it easy to compare wildly different approaches. In so doing, the authors establish previously unreported relations between these methods and are able to present a fast-growing literature in a very compact way. Furthermore, the authors discuss how best to evaluate cross-lingual word embedding methods and survey the resources available for students and researchers interested in this topic.
| Author | : Bharathi Raja Chakravarthi |
| Publisher | : Springer Nature |
| Total Pages | : 470 |
| Release | : |
| Genre | : |
| ISBN | : 3031584953 |
| Author | : Vinit Kumar Gunjan |
| Publisher | : Springer Nature |
| Total Pages | : 821 |
| Release | : 2022-01-10 |
| Genre | : Technology & Engineering |
| ISBN | : 9811664072 |
This book contains original, peer-reviewed research articles from the Second International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, held in March 28-29th 2021 at CMR Institute of Technology, Hyderabad, Telangana India. It covers the latest research trends and developments in areas of machine learning, artificial intelligence, neural networks, cyber-physical systems, cybernetics, with emphasis on applications in smart cities, Internet of Things, practical data science and cognition. The book focuses on the comprehensive tenets of artificial intelligence, machine learning and deep learning to emphasize its use in modelling, identification, optimization, prediction, forecasting and control of future intelligent systems. Submissions were solicited of unpublished material, and present in-depth fundamental research contributions from a methodological/application perspective in understanding artificial intelligence and machine learning approaches and their capabilities in solving a diverse range of problems in industries and its real-world applications.
| Author | : Philipp Koehn |
| Publisher | : Cambridge University Press |
| Total Pages | : 409 |
| Release | : 2020-06-18 |
| Genre | : Computers |
| ISBN | : 1108497322 |
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
| Author | : Mohammad Taher Pilehvar |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 177 |
| Release | : 2020-11-13 |
| Genre | : Computers |
| ISBN | : 1636390226 |
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.
| Author | : Katarzyna Marszałek-Kowalewska |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 258 |
| Release | : 2023-05-22 |
| Genre | : Language Arts & Disciplines |
| ISBN | : 3110616718 |
In this series, Iranian languages and linguistics take centre stage. Each volume is dedicated to a key topic and brings together leading experts from around the globe.
| Author | : Maciej Paszynski |
| Publisher | : Springer Nature |
| Total Pages | : 609 |
| Release | : 2021-06-11 |
| Genre | : Computers |
| ISBN | : 3030779645 |
The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually. Chapter “Effective Solution of Ill-posed Inverse Problems with Stabilized Forward Solver” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
| Author | : Marcos Zampieri |
| Publisher | : Cambridge University Press |
| Total Pages | : 345 |
| Release | : 2021-09-02 |
| Genre | : Computers |
| ISBN | : 1108429351 |
Studying language variation requires comprehensive interdisciplinary knowledge and new computational tools. This essential reference introduces researchers and graduate students in computer science, linguistics, and NLP to the core topics in language variation and the computational methods applied to similar languages, varieties, and dialects.
| Author | : Seizi Iwata |
| Publisher | : John Benjamins Publishing |
| Total Pages | : 258 |
| Release | : 2008-06-09 |
| Genre | : Language Arts & Disciplines |
| ISBN | : 9027291047 |
The aim of the present volume is two-fold: to give a coherent account of the locative alternation in English, and to develop a constructional theory that overcomes a number of problems in earlier constructional accounts. The lexical-constructional account proposed here is characterized by two main features. On the one hand, it emphasizes the need for a detailed examination of verb meanings. On the other, it introduces lower-level constructions such as verb-class-specific constructions and verb-specific constructions, and makes full use of these lower-level constructions in accounting for alternation phenomena. Rather than being a completely new version of construction grammar, the proposed lexical-constructional account is an automatic consequence of the basic tenet of constructional approaches as being usage-based.
| Author | : Emil Hvitfeldt |
| Publisher | : CRC Press |
| Total Pages | : 402 |
| Release | : 2021-10-22 |
| Genre | : Computers |
| ISBN | : 1000461971 |
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
