Issues in Analogical Inference Over Sequences of Symbols: A Case Study on Proper Name Transliteration

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 548)

Abstract

Formal analogies, that is, proportional analogies involving relations at a formal level (e.g. cordially is to cordial as appreciatively is to appreciative) have a long history in Linguistics. They can accommodate a wide variety of linguistic data without resorting to ad hoc representations and are inherently good at capturing long range dependencies between data. Unfortunately, applying analogical learning on top of formal analogy to current Natural Language Processing (NLP) tasks, which often involve massive amount of data, is quite challenging. In this chapter, we draw on our previous works and identify some issues that remain to be addressed for formal analogy to stand by itself in the landscape of NLP. As a case study, we monitor our current implementation of analogical learning on a task of transliterating English proper names into Chinese.

Notes

Acknowledgments

This work has been partially founded by the Natural Sciences and Engineering Research Council of Canada (NSERC). We are grateful to the anonymous reviewers of the short paper submitted to the 2012 SAMAI workshop, as well as those that reviewed this article. We found one review in particular especially inspiring.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Université de MontrealMontrealCanada
  2. 2.LIMSI/CNRSUniversité Paris SudOrsayFrance

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