natural-language-processing-8
Semantic Similarity
3260 papers • 126 benchmarks • 313 datasets
The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods. Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
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These leaderboards are used to track progress in semantic-similarity-8
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Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)
Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)
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iUse these libraries to find semantic-similarity-8 models and implementations
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Most implemented papers
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Iryna Gurevych, Nils Reimers•Tue Aug 13 2019
Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity is presented.
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