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Abstract: Classifying semantic relations between entity pairs in sentences is animportant task in Natural Language Processing (NLP). Most previous models forrelation classification rely on the high-level lexical and syntactic featuresobtained by NLP tools such as WordNet, dependency parser, part-of-speech (POS)tagger, and named entity recognizers (NER). In addition, state-of-the-artneural models based on attention mechanisms do not fully utilize information ofentity that may be the most crucial features for relation classification. Toaddress these issues, we propose a novel end-to-end recurrent neural modelwhich incorporates an entity-aware attention mechanism with a latent entitytyping (LET) method. Our model not only utilizes entities and their latenttypes as features effectively but also is more interpretable by visualizingattention mechanisms applied to our model and results of LET. Experimentalresults on the SemEval-2010 Task 8, one of the most popular relationclassification task, demonstrate that our model outperforms existingstate-of-the-art models without any high-level features.
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Download crs electronic cameras. Joohong Min Jinkook Lee This guide reviews and summarizes questions on informal/non-institutional care/help from the Health and Retirement Study (HRS) and its sister surveys.

From: Joohong Lee [view email]
[v1]Wed, 23 Jan 2019 23:19:45 UTC (1,032 KB)
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