sts17 en ua Benchmark (Semantic Similarity)

0 rows 0 with code listed 2 metrics

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

The archive carries no text for this table; the description above is the archive's text for the task Semantic Similarity. archive 2025-07-28

Results archive 2025-07-28

No rows in the archive for this table at snapshot 2025-07-28. It declares 2 metrics (Pearson Cosine, Spearman Cosine) but no result was ever recorded against it. That says nothing about whether results exist elsewhere.

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