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• W2468799129 abstract "A growing body of research has recently been conducted on semantic textual similarity using a variety of neural network models. While re- cent research focuses on word-based represen- tation for phrases, sentences and even paragraphs, this study considers an alternative approach based on character n-grams. We generate embeddings for character n-grams using a continuous-bag-of-n-grams neural network model. Three different sentence rep- resentations based on n-gram embeddings are considered. Results are reported for experi- ments with bigram, trigram and 4-gram em- beddings on the STS Core dataset for SemEval-2016 Task 1." @default.
• W2468799129 created "2016-07-22" @default.
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• W2468799129 date "2016-01-01" @default.
• W2468799129 modified "2023-09-23" @default.
• W2468799129 title "ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity" @default.
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• W2468799129 doi "https://doi.org/10.18653/v1/s16-1114" @default.
• W2468799129 hasPublicationYear "2016" @default.
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