2015 FindingFunctioninFormCompositio

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Subject Headings: n-Gram Character Embedding System, Word Embedding System.

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Abstract

We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type, our model requires only a single vector per character type and a fixed set of parameters for the compositional model. Despite the compactness of this model and, more importantly, the arbitrary nature of the form-function relationship in language, our "composed" word representations yield state-of-the-art results in language modeling and part-of-speech tagging. Benefits over traditional baselines are particularly pronounced in morphologically rich languages (e.g., Turkish).

References

BibTeX

@inproceedings{2015_FindingFunctioninFormCompositio,
  author    = {Wang Ling and
               Chris Dyer and
               Alan W. Black and
               Isabel Trancoso and
               Ramon Fermandez and
               Silvio Amir and
               Luis Marujo and
               Tiago Luis},
  editor    = {Lluis Marquez and
               Chris Callison{-}Burch and
               Jian Su and
               Daniele Pighin and
               Yuval Marton},
  title     = {Finding Function in Form: Compositional Character Models for Open
               Vocabulary Word Representation},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural
               Language Processing (EMNLP 2015)},
  pages     = {1520--1530},
  publisher = {The Association for Computational Linguistics},
  year      = {2015},
  url       = {https://doi.org/10.18653/v1/d15-1176},
  doi       = {10.18653/v1/d15-1176},
}


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 FindingFunctioninFormCompositioChris Dyer
Wang Ling
Alan W. Black
Isabel Trancoso
Ramon Fermandez
Silvio Amir
Luis Marujo
Tiago Luis
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation2015