Name
Title | ||
|---|---|---|
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity. |
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. |
Year | Venue | Field |
|---|---|---|
2016 | SemEval@NAACL-HLT | SemEval,Computer science,Trigram,Speech recognition,n-gram,Artificial intelligence,Natural language processing,Bigram,Artificial neural network,Sentence,Machine learning |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asli Eyecioglu | 1 | 8 | 0.81 |
| Bill Keller | 2 | 87 | 6.26 |