Morpheme Embedding System

A Morpheme Embedding System is a Morpheme Detection System that can map a morpheme from a text item into a vector representation.

• AKA: Morpheme Vector Representation System, Morphemic Representation System.
• Context:
  • It can solve a Morpheme Embedding Task by implementing a Morpheme Embedding Algorithm.
  • It is based on a Language Modeling System and Feature Learning System.
• Example(s):
  • MorphoRNN Embedding System,
  • …
• Counter-Example(s):
  • Word Embedding System,
  • Subword Embedding System,
  • Character Embedding System.
• See: Morpheme Vector, Morpheme Embedding Model, Natural Language Processing System, Word Detection System, Language Model, POS Tagging System.

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References

2013

• (Luong et al., 2013) ⇒ Thang Luong, Richard Socher, and Christopher Manning. (2013). “Better Word Representations with Recursive Neural Networks for Morphology.” In: Proceedings of the Seventeenth Conference on Computational Natural Language Learning (CoNLL-2013).
  • QUOTE: Our morphological Recursive Neural Network (morphoRNN) is similar to (Socher et al., 2011b), but operates at the morpheme level instead of at the word level. Specifically, morphemes, the minimum meaning-bearing unit in languages, are modeled as real-valued vectors of parameters, and are used to build up more complex words. We assume access to a dictionary of morphemic analyses of words, which will be detailed in Section 4.

    Following (Collobert and Weston, 2008), distinct morphemes are encoded by column vectors in a morphemic embedding matrix $\mathbf{W}_e \in \R^{d\times |M|}, where $d$ is the vector dimension and $M$ is an ordered set of all morphemes in a language.

    As illustrated in Figure 1, vectors of morphologically complex words are gradually built up from their morphemic representations (...).

Figure 1: Morphological Recursive Neural Network. A vector representation for the word “unfortunately” is constructed from morphemic vectors: unpre, fortunatestm, lysuf. Dotted nodes are computed on-the-fly and not in the lexicon.

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