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Using Resource-Rich Languages to Improve Morphological Analysis of Under-Resourced Languages
lib:191308dd85be3832 (v1.0.0)
Authors: Peter Baumann,Janet Pierrehumbert
Where published: LREC 2014 5
Document: PDF DOI
Abstract URL: https://www.aclweb.org/anthology/L14-1035/
The world-wide proliferation of digital communications has created the need for language and speech processing systems for under-resourced languages. Developing such systems is challenging if only small data sets are available, and the problem is exacerbated for languages with highly productive morphology. However, many under-resourced languages are spoken in multi-lingual environments together with at least one resource-rich language and thus have numerous borrowings from resource-rich languages. Based on this insight, we argue that readily available resources from resource-rich languages can be used to bootstrap the morphological analyses of under-resourced languages with complex and productive morphological systems. In a case study of two such languages, Tagalog and Zulu, we show that an easily obtainable English wordlist can be deployed to seed a morphological analysis algorithm from a small training set of conversational transcripts. Our method achieves a precision of 100{\%} and identifies 28 and 66 of the most productive affixes in Tagalog and Zulu, respectively.
Where published: LREC 2014 5
Document: PDF DOI
Abstract URL: https://www.aclweb.org/anthology/L14-1035/
The world-wide proliferation of digital communications has created the need for language and speech processing systems for under-resourced languages. Developing such systems is challenging if only small data sets are available, and the problem is exacerbated for languages with highly productive morphology. However, many under-resourced languages are spoken in multi-lingual environments together with at least one resource-rich language and thus have numerous borrowings from resource-rich languages. Based on this insight, we argue that readily available resources from resource-rich languages can be used to bootstrap the morphological analyses of under-resourced languages with complex and productive morphological systems. In a case study of two such languages, Tagalog and Zulu, we show that an easily obtainable English wordlist can be deployed to seed a morphological analysis algorithm from a small training set of conversational transcripts. Our method achieves a precision of 100{\%} and identifies 28 and 66 of the most productive affixes in Tagalog and Zulu, respectively.
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