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Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER
Biomedical Named Entities are complex, so approximate matching has been used to improve entity coverage.
Improving Sequence to Sequence Neural Machine Translation by Utilizing Syntactic Dependency Information
In order to assess the performance, we construct model based on an attention mechanism encoder-decoder model in which the source language is input to the encoder as a sequence and the decoder generates the target language as a linearized dependency tree structure.
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