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• W281284504 abstract "Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) hand-crafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a log-linear classifier with hand-crafted features gives state-of-the-art results." @default.
• W281284504 created "2016-06-24" @default.
• W281284504 creator A5024437840 @default.
• W281284504 creator A5027142928 @default.
• W281284504 creator A5061241015 @default.
• W281284504 date "2015-05-10" @default.
• W281284504 modified "2023-09-27" @default.
• W281284504 title "Improved Relation Extraction with Feature-Rich Compositional Embedding Models" @default.
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• W281284504 hasPublicationYear "2015" @default.
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