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3326 results in Artificial Intelligence and Natural Language Processing

Page 49 of 167

  • NLE volume 25 issue 2 Cover and Front matter

  • Journal:
    Natural Language Engineering / Volume 25 / Issue 2 / March 2019
    Published online by Cambridge University Press:
    01 April 2019, pp. f1-f2
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  • GANs vs. good enough

  • Kenneth Ward Church
  • Journal:
    Natural Language Engineering / Volume 25 / Issue 2 / March 2019
    Published online by Cambridge University Press:
    01 April 2019, pp. 323-329
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  • General Adversarial Networks are hot. Given Murphy’s Law, it is prudent to be paranoid. Best not to design for the average case. There is a long tradition of designing for the hundred-year flood (and five 9s reliability). What is good enough? Historically, the market hasn’t been willing to pay for five 9s. Hard to justify upfront costs for future benefits that will only payoff under unlikely scenarios, and might not work when needed. If the market isn’t willing to pay for five 9s, can we afford to design for the worst case?

  • Effectiveness of data-driven induction of semantic spaces and traditional classifiers for sarcasm detection

  • Mattia Antonino Di Gangi, Giosué Lo Bosco, Giovanni Pilato
  • Journal:
    Natural Language Engineering / Volume 25 / Issue 2 / March 2019
    Published online by Cambridge University Press:
    01 April 2019, pp. 257-285

  • Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labeled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses their own datasets to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine-learning algorithms to input texts sub-symbolically represented in a Latent Semantic space. The main consequence is that our studies establish both reference datasets and baselines for the sarcasm detection problem that could serve the scientific community to test newly proposed methods.

  • NLE volume 25 issue 2 Cover and Back matter

  • Journal:
    Natural Language Engineering / Volume 25 / Issue 2 / March 2019
    Published online by Cambridge University Press:
    01 April 2019, pp. b1-b2
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  • Weighted finite-state transducers for normalization of historical texts

  • Izaskun Etxeberria, Iñaki Alegria, Larraitz Uria
  • Journal:
    Natural Language Engineering / Volume 25 / Issue 2 / March 2019
    Published online by Cambridge University Press:
    01 April 2019, pp. 307-321

  • This paper presents a study about methods for normalization of historical texts. The aim of these methods is learning relations between historical and contemporary word forms. We have compiled training and test corpora for different languages and scenarios, and we have tried to read the results related to the features of the corpora and languages. Our proposed method, based on weighted finite-state transducers, is compared to previously published ones. Our method learns to map phonological changes using a noisy channel model; it is a simple solution that can use a limited amount of supervision in order to achieve adequate performance. The compiled corpora are ready to be used for other researchers in order to compare results. Concerning the amount of supervision for the task, we investigate how the size of training corpus affects the results and identify some interesting factors to anticipate the difficulty of the task.

Page 49 of 167