Convolutional Semantic Similarity Network Entity Linking System
(Redirected from CNN-SNN Entity Linking System)
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A Convolutional Semantic Similarity Network Entity Linking System is an Entity Linking System that can detect entities in a text document and create links between them by training a Convolutional Semantic Similarity Neural Network.
- AKA: CNN-SSN Entity Linking System.
- Example(s):
- Counter-Example(s):
- See: Semantic Similarity Measure, Taxonomy, Directed Graph, Semantic Word Similarity, Semantic Word Similarity Benchmark Task.
References
2016
- (Francis-Landau et al., 2016) ⇒ Matthew Francis-Landau, Greg Durrett, and Dan Klein. (2016). “Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks.” In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2016).
- QUOTE: Our model focuses on two core ideas: first, that topic semantics at different granularities in a document are helpful in determining the genres of entities for entity linking, and second, that CNNs can distill a block of text into a meaningful topic vector.
(...) As shown in the middle of Figure 1, each feature in $f_C$ is a cosine similarity between a topic vector associated with the source document and a topic vector associated with the target entity. These vectors are computed by distinct CNNs operating over different subsets of relevant text.
- QUOTE: Our model focuses on two core ideas: first, that topic semantics at different granularities in a document are helpful in determining the genres of entities for entity linking, and second, that CNNs can distill a block of text into a meaningful topic vector.