Disambiguating Spatial Prepositions Using Deep Convolutional Networks

AAAI 2017 pp. 3209-3215

doi:10.1609/AAAI.V31I1.10973 /aaai/2017/hassani2017aaai-disambiguating/

Abstract

We address the coarse-grained disambiguation of the spatial prepositions as the first step towards spatial role labeling using deep learning models. We propose a hybrid feature of word embeddings and linguistic features, and compare its performance against a set of linguistic features, pre-trained word embeddings, and corpus-trained embeddings using seven classical machine learning classifiers and two deep learning models. We also compile a dataset of 43,129 sample sentences from Pattern Dictionary of English Prepositions (PDEP). The comprehensive experimental results suggest that the combination of the hybrid feature and a convolutional neural network outperforms state-of-the-art methods and reaches the accuracy of 94.21% and F1-score of 0.9398.

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Cite

Text

Hassani and Lee. "Disambiguating Spatial Prepositions Using Deep Convolutional Networks." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10973

Markdown

[Hassani and Lee. "Disambiguating Spatial Prepositions Using Deep Convolutional Networks." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/hassani2017aaai-disambiguating/) doi:10.1609/AAAI.V31I1.10973

BibTeX

@inproceedings{hassani2017aaai-disambiguating,
  title     = {{Disambiguating Spatial Prepositions Using Deep Convolutional Networks}},
  author    = {Hassani, Kaveh and Lee, Won-Sook},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {3209-3215},
  doi       = {10.1609/AAAI.V31I1.10973},
  url       = {https://mlanthology.org/aaai/2017/hassani2017aaai-disambiguating/}
}