Unsupervised Morphological Expansion of Small Datasets for Improving Word Embeddings

نویسندگان

  • Syed Sarfaraz Akhtar
  • Arihant Gupta
  • Avijit Vajpayee
  • Arjit Srivastava
  • Manish Shrivastava
چکیده

We present a language independent, unsupervised method for building word embeddings using morphological expansion of text. Our model handles the problem of data sparsity and yields improved word embeddings by relying on training word embeddings on artificially generated sentences. We evaluate our method using small sized training sets on eleven test sets for the word similarity task across seven languages. Further, for English, we evaluated the impacts of our approach using a large training set on three standard test sets. Our method improved results across all languages.

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عنوان ژورنال:
  • CoRR

دوره abs/1711.05678  شماره 

صفحات  -

تاریخ انتشار 2017