Papers by Shima Asaadi
Big BiRD: A Large, Fine-Grained, Bigram Relatedness Dataset for Examining Semantic Composition (N19-1)
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| Challenge: | Existing datasets of semantic relatedness only include pairs of unigrams (single words) Existing data suffer from inconsistent annotations and scale region bias due to rating scales. |
| Approach: | They propose to use a large, fine-grained, bigram relatedness dataset to compare the relatedness of 3,345 English term pairs using a comparative annotation technique called Best–Worst Scaling. |
| Outcome: | The proposed datasets are highly reliable and have a split-half reliability of 0.937. |