Natural language and machine translation

In recent years, online social networking has revolutionized interpersonal communication.

In recent years, online social networking has revolutionized interpersonal communication.

Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms.

Meaning is a fundamental concept in Natural Language Processing (NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG).

Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language.

The volume of natural language text data has been rapidly increasing over the past two decades, due to factors such as the growth of the Web, the low cost associated with publishing, and the progress on the digitization of printed texts.

Many applications within natural language processing involve performing text-to-text transformations, i.

Meaning is a fundamental concept in Natural Language Processing (NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG).

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.

Creating your own domain-specific languages (DSLs) is both challenging and exhilarating.

Creating your own domain-specific languages (DSLs) is both challenging and exhilarating.

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Este livro tem como objetivo apresentar as técnicas fundamentais de processamento de linguagem natural, desenvolver uma compreensão dos limites dessas técnicas e dos problemas de pesquisa atuais e avaliar algumas aplicações atuais e potenciais.

No detailed description available for "e;The AI Value Playbook"e;.

Empirical methods are means to answering methodological questions of empirical sciences by statistical techniques.

Labelling data is one of the most fundamental activities in science, and has underpinned practice, particularly in medicine, for decades, as well as research in corpus linguistics since at least the development of the Brown corpus.

While great strides have been made in the field of search and recommendation, there are still challenges and opportunities to address information access issues that involve solving tasks and accomplishing goals for a wide variety of users.

Opportunity and Curiosity find similar rocks on Mars.

This book provides a comprehensive introduction to Conversational AI.

Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP).

This book presents a taxonomy framework and survey of methods relevant to explaining the decisions and analyzing the inner workings of Natural Language Processing (NLP) models.

This book discusses the state of the art of automated essay scoring, its challenges and its potential.

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query.

A comprehensive guide on Transformer, BERT, and GPT models, including ChatGPT and prompt engineering, essential for mastering advanced AI.

A lot of digital ink has been spilled on "e;big data"e; over the past few years.

This book conveys the fundamentals of Linked Lexical Knowledge Bases (LLKB) and sheds light on their different aspects from various perspectives, focusing on their construction and use in natural language processing (NLP).

Thanks to the availability of texts on the Web in recent years, increased knowledge and information have been made available to broader audiences.

This book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications.

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data.

Many NLP tasks have at their core a subtask of extracting the dependencies-who did what to whom-from natural language sentences.

Learning to rank refers to machine learning techniques for training a model in a ranking task.

This unique book provides a comprehensive introduction to the most popular syntax-based statistical machine translation models, filling a gap in the current literature for researchers and developers in human language technologies.

Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments---most of which demand high cognitive skills (e.

The attempt to spot deception through its correlates in human behavior has a long history.

The World Wide Web constitutes the largest existing source of texts written in a great variety of languages.

Discover how to master advanced spaCy techniques, including custom pipelines, LLM integration, and model training, to build NLP solutions efficiently and effectivelyKey FeaturesBuild End-to-End NLP Workflows, From Local Development to Production with Weasel and FastAPIMaster No-Training NLP Development with spaCy-LLM, From Prompt Engineering to Custom TasksCreate Advanced NLP Solutions, From Custom Components to Neural Coreference ResolutionBook DescriptionMastering spaCy, Second Edition is your comprehensive guide to building sophisticated NLP applications using the spaCy ecosystem.

It has been estimated that over a billion people are using or learning English as a second or foreign language, and the numbers are growing not only for English but for other languages as well.

Membrane computing is a class of distributed and parallel computing models inspired by living cells.

For humans, understanding a natural language sentence or discourse is so effortless that we hardly ever think about it.

This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data.

The World Wide Web constitutes the largest existing source of texts written in a great variety of languages.

People make sense of a text by identifying the semantic relations which connect the entities or concepts described by that text.

This book provides a thorough introduction to the subfield of theoretical computer science known as grammatical inference from a computational linguistic perspective.

The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity.

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language.

Discourse Processing here is framed as marking up a text with structural descriptions on several levels, which can serve to support many language-processing or text-mining tasks.

This book provides an overview of various techniques for the alignment of bitexts.

A major part of natural language processing now depends on the use of text data to build linguistic analyzers.

Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications.

Human language acquisition has been studied for centuries, but using computational modeling for such studies is a relatively recent trend.

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential.