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Characters or morphemes: how to represent words?
Üstün, Ahmet Kurfalı, Murathan Can, Burcu
Üstün, Ahmet
Kurfalı, Murathan
Can, Burcu
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2018
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In this paper, we investigate the effects of using subword information in representation learning. We argue that using syntactic subword units effects the quality of the word representations positively. We introduce a morpheme-based model and compare it against to word-based, character-based, and character n-gram level models. Our model takes a list of candidate segmentations of a word and learns the representation of the word based on different segmentations that are weighted by an attention mechanism. We performed experiments on Turkish as a morphologically rich language and English with a comparably poorer morphology. The results show that morpheme-based models are better at learning word representations of morphologically complex languages compared to character-based and character n-gram level models since the morphemes help to incorporate more syntactic knowledge in learning, that makes morpheme-based models better at syntactic tasks.
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Üstün, A., Kurfalı, M. and Can, B. (2018) Characters or morphemes: how to represent words? In, Proceedings of The Third Workshop on Representation Learning for NLP, Augenstein, I., Cao, K., He, H., Hill, F. et al. Stroudsburg, PA: Association for Computational Linguistics, pp. 144-153.
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en
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© 2018 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: http://dx.doi.org/10.18653/v1/W18-3019
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9781948087438
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This research was supported by TUBITAK (The Scientific and Technological Research Council of Turkey) grant number 115E464.