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3312 results in Artificial Intelligence and Natural Language Processing

Page 32 of 166

  • Efficient bilingual lexicon extraction from comparable corpora based on formal concepts analysis

  • Mohamed Chebel, Chiraz Latiri, Eric Gaussier
  • Journal:
    Natural Language Engineering / Volume 29 / Issue 1 / January 2023
    Published online by Cambridge University Press:
    04 October 2021, pp. 138-161

  • Bilingual corpora are an essential resource used to cross the language barrier in multilingual natural language processing tasks. Among bilingual corpora, comparable corpora have been the subject of many studies as they are both frequent and easily available. In this paper, we propose to make use of formal concept analysis to first construct concept vectors which can be used to enhance comparable corpora through clustering techniques. We then show how one can extract bilingual lexicons of improved quality from these enhanced corpora. We finally show that the bilingual lexicons obtained can complement existing bilingual dictionaries and improve cross-language information retrieval systems.

  • Evaluation of taxonomic and neural embedding methods for calculating semantic similarity

  • Dongqiang Yang, Yanqin Yin
  • Journal:
    Natural Language Engineering / Volume 28 / Issue 6 / November 2022
    Published online by Cambridge University Press:
    28 September 2021, pp. 733-761

  • Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity.

Vision
  • Edited by Andrew Fabian, Janet Gibson, Mike Sheppard, Simone Weyand
  • With contributions by Andrew Blake , Carolin Crawford , Paul Fletcher , Sophie Hackford , Anya Hurlbert , Dan-Eric Nilsson and Carlo Rovelli
  • Published online:
    17 September 2021
    Print publication:
    26 August 2021
  • Arising from the 2019 Darwin College Lectures, this book presents essays from seven prominent public intellectuals on the theme of vision. Each author examines this theme through the lens of their own particular area of expertise, making for a lively interdisciplinary volume including chapters on neuroscience, colour perception, biological evolution, astronomy, the future of technology, computer vision, and the visionary core of science. Featuring contributions by professors of neuroscience Paul Fletcher and Anya Hurlbert, professor of zoology Dan-Eric Nilsson, the futurist Sophie Hackford, Microsoft distinguished scientist Andrew Blake, theoretical physicist and author Carlo Rovelli, and Dr Carolin Crawford, the Public Astronomer at the University of Cambridge, this volume will be of interest to anybody curious about how we see the world.

Page 32 of 166