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2 results

  • 10 - Recurrent Neural Networks

  • Mihai Surdeanu, University of Arizona, Marco Antonio Valenzuela-Escárcega, University of Arizona
  • Book:
    Deep Learning for Natural Language Processing
    Published online:
    01 February 2024
    Print publication:
    08 February 2024, pp 147-164

  • Summary

    Up to this point, we have only discussed neural approaches for text classification (e.g., review and news classification) that handle the text as a bag of words. That is, we aggregate the words either by representing them as explicit features in a feature vector or by averaging their numerical representations (i.e., embeddings). Although this strategy completely ignores the order in which words occur in a sentence, it has been repeatedly shown to be a good solution for many practical natural language processing applications that are driven by text classification. Nevertheless, for many natural language processing tasks such as part-of-speech tagging, we need to capture the word-order information more explicitly. Sequence models capture exactly this scenario, where classification decisions must be made using not only the current information but also the context in which it appears. In particular, we discuss several types of recurrent neural networks, including stacked (or deep) recurrent neural networks, bidirectional recurrent neural networks, and long short-term memory networks. Last, we introduced conditional random fields, which extend recurrent neural networks with an extra layer that explicitly models transition probabilities between two cells.

  • Recognition of visual scene elements from a story text in Persian natural language

  • Mojdeh Hashemi-Namin, Mohammad Reza Jahed-Motlagh, Adel Torkaman Rahmani
  • Journal:
    Natural Language Engineering / Volume 29 / Issue 3 / May 2023
    Published online by Cambridge University Press:
    24 August 2022, pp. 693-719
    • Article
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  • Text-to-scene conversion systems map natural language text to formal representations required for visual scenes. The difficulty involved in this mapping is one of the most critical challenges for developing these systems. The current study mapped Persian natural language text as the headmost system to a conceptual scene model. This conceptual scene model is an intermediate semantic representation between natural language and the visual scene and contains descriptions of visual elements of the scene. It will be used to produce meaningful animation based on an input story in this ongoing study. The mapping task was modeled as a sequential labeling problem, and a conditional random field (CRF) model was trained and tested for sequential labeling of scene model elements. To the best of the authors’ knowledge, no dataset for this task exists; thus, the required dataset was collected for this task. The lack of required off-the-shelf natural language processing modules and a significant error rate in the available corpora were important challenges to dataset collection. Some features of the dataset were manually annotated. The results were evaluated using standard text classification metrics, and an average accuracy of 85.7% was obtained, which is satisfactory.