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Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings

What it is about

The authors extract the root of a Semitic word with a sequence-to-sequence model (a BiGRU encoder and a GRU decoder over characters). The novelty is constrained beam search: a trie built from the dictionary of valid roots prunes every decoding step, so the output is always a real root. The model is compared with multiclass classifiers, unconstrained seq2seq models and rule-based stemmers. Five-fold accuracy is 98.8% on a Hebrew word-root list of about 11,500 words (roughly 500 roots, Wiktionary-derived) and 83.2% on an Arabic list of 140K words (11K roots). On rare Arabic roots (three or fewer training occurrences) and on unseen roots the constrained model still does best. The root is then used to split words into prefix, stem and suffix slots, and these slots enrich FastText-style embeddings, which helps on morphemic analogies and word similarity.

Ideas

For Mova

I1 is a rule for The verb and The Noun, the Adjective and the Numeral (root check); I2 belongs in The weak verbs and The Noun, the Adjective and the Numeral; I3 is a measure for tools/hbo. The Wiktionary-derived root list is CC BY-SA data: it may be tested against and learned from, but it is not carried into a release.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/w19-4610

@inproceedings{elkishky2019constrained,
  title = {{Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings}},
  author = {El-Kishky, Ahmed and Fu, Xingyu and Addawood, Aseel and Sobh, Nahil and Voss, Clare and Han, Jiawei},
  booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Workshop},
  year = {2019},
  doi = {10.18653/v1/w19-4610},
  url = {https://doi.org/10.18653/v1/w19-4610}
}

References

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