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- W71098462 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." @default.
- W71098462 created "2016-06-24" @default.
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- W71098462 date "2014-01-01" @default.
- W71098462 modified "2023-09-30" @default.
- W71098462 title "Issues in Analogical Inference Over Sequences of Symbols: A Case Study on Proper Name Transliteration" @default.
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- W71098462 doi "https://doi.org/10.1007/978-3-642-54516-0_3" @default.
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