Computational and corpus linguistics

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.

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

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.

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.

Weighted finite-state transducers (WFSTs) are commonly used by engineers and computational linguists for processing and generating speech and text.

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

This book provides a comprehensive introduction to Conversational AI.

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.

Text production has many applications.

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.

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.

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

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

Opportunity and Curiosity find similar rocks on Mars.

Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field.

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

Neural networks are a family of powerful machine learning models.

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.

This book covers the topic of temporal tagging, the detection of temporal expressions and the normalization of their semantics to some standard format.

The literary imagination may take flight on the wings of metaphor, but hard-headed scientists are just as likely as doe-eyed poets to reach for a metaphor when the descriptive need arises.

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

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

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.

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

In the last few years, a number of NLP researchers have developed and participated in the task of Recognizing Textual Entailment (RTE).

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

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.

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