Package smile.nlp.embedding

Interface GloVe

public interface GloVe
Global Vectors for Word Representation. GloVe is an unsupervised learning algorithm for obtaining vector representations for words.

GloVe is essentially a log-bilinear model with a weighted least-squares objective. The main intuition underlying the model is the simple observation that ratios of word-word co-occurrence probabilities have the potential for encoding some form of meaning.

Training is performed on aggregated global word-word co-occurrence statistics from a corpus. The training objective of GloVe is to learn word vectors such that their dot product equals the logarithm of the words' probability of co-occurrence. Owing to the fact that the logarithm of a ratio equals the difference of logarithms, this objective associates (the logarithm of) ratios of co-occurrence probabilities with vector differences in the word vector space. Because these ratios can encode some form of meaning, this information gets encoded as vector differences as well.

  • Method Summary

    Static Methods
    Modifier and Type
    Method
    Description
    static Word2Vec
    of(Path file)
    Loads a GloVe model.

  • Method Details

    • of

      static Word2Vec of(Path file) throws IOException
      Loads a GloVe model.
      Parameters:
      file - the path to model file.
      Returns:
      the GloVe model.
      Throws:
      IOException - when fails to read the file.