Name
Title | ||
|---|---|---|
Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement. |
Abstract | ||
|---|---|---|
Textual similarity measurement is a challenging problem, as it requires understanding the semantics of input sentences. Most previous neural network models use coarse-grained sentence modeling, which has difficulty capturing fine-grained word-level information for semantic comparisons. As an alternative, we propose to explicitly model pairwise word interactions and present a novel similarity focus mechanism to identify important correspondences for better similarity measurement. Our ideas are implemented in a novel neural network architecture that demonstrates state-ofthe-art accuracy on three SemEval tasks and two answer selection tasks. |
Year | Venue | Field |
|---|---|---|
2016 | HLT-NAACL | Semantic similarity,Pairwise comparison,SemEval,Computer science,Neural network architecture,Natural language processing,Artificial intelligence,Artificial neural network,Sentence,Deep neural networks,Semantics,Machine learning |
DocType | Citations | PageRank |
Conference | 50 | 1.24 |
References | Authors | |
48 | 2 | |