Shallow Semantic Text Parsing Task

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A Shallow Semantic Text Parsing Task is a semantic text parsing task that is a shallow parsing task (that requires a shallow meaning representation).



References

2024

  • (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Semantic_parsing#Shallow_Semantic_Parsing Retrieved:2024-1-24.
    • Shallow semantic parsing is concerned with identifying entities in an utterance and labelling them with the roles they play. Shallow semantic parsing is sometimes known as slot-filling or frame semantic parsing, since its theoretical basis comes from frame semantics, wherein a word evokes a frame of related concepts and roles. Slot-filling systems are widely used in virtual assistants in conjunction with intent classifiers, which can be seen as mechanisms for identifying the frame evoked by an utterance.[1] [2] Popular architectures for slot-filling are largely variants of an encoder-decoder model, wherein two recurrent neural networks (RNNs) are trained jointly to encode an utterance into a vector and to decode that vector into a sequence of slot labels. [3] This type of model is used in the Amazon Alexa spoken language understanding system.[1] This parsing follow an unsupervised learning techniques.

2013


  1. 1.0 1.1 Kumar, Anjishnu, et al. "Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding." arXiv preprint arXiv:1711.00549 (2017).
  2. Bapna, Ankur, et al. "Towards zero-shot frame semantic parsing for domain scaling." arXiv preprint arXiv:1707.02363(2017).
  3. Liu, Bing, and Ian Lane. "Attention-based recurrent neural network models for joint intent detection and slot filling." arXiv preprint arXiv:1609.01454 (2016).