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3312 results in Artificial Intelligence and Natural Language Processing

Page 37 of 166

  • Recent advances in processing negation

  • Roser Morante, Eduardo Blanco
  • Journal:
    Natural Language Engineering / Volume 27 / Issue 2 / March 2021
    Published online by Cambridge University Press:
    17 December 2020, pp. 121-130
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  • Negation is a complex linguistic phenomenon present in all human languages. It can be seen as an operator that transforms an expression into another expression whose meaning is in some way opposed to the original expression. In this article, we survey previous work on negation with an emphasis on computational approaches. We start defining negation and two important concepts: scope and focus of negation. Then, we survey work in natural language processing that considers negation primarily as a means to improve the results in some task. We also provide information about corpora containing negation annotations in English and other languages, which usually include a combination of annotations of negation cues, scopes, foci, and negated events. We continue the survey with a description of automated approaches to process negation, ranging from early rule-based systems to systems built with traditional machine learning and neural networks. Finally, we conclude with some reflections on current progress and future directions.

Natural Language Processing
  • A Machine Learning Perspective
  • Yue Zhang, Zhiyang Teng
  • Published online:
    16 December 2020
    Print publication:
    07 January 2021
  • With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.

  • NLE volume 27 issue 1 Cover and Front matter

  • Journal:
    Natural Language Engineering / Volume 27 / Issue 1 / January 2021
    Published online by Cambridge University Press:
    15 December 2020, pp. f1-f2
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Page 37 of 166