DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity Estimation
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چکیده
This paper describes our submission for the *SEM shared task of Semantic Textual Similarity. We estimate the semantic similarity between two sentences using regression models with features: 1) n-gram hit rates (lexical matches) between sentences, 2) lexical semantic similarity between non-matching words, 3) string similarity metrics, 4) affective content similarity and 5) sentence length. Domain adaptation is applied in the form of independent models and a model selection strategy achieving a mean correlation of 0.47.
منابع مشابه
DeepPurple: Estimating Sentence Semantic Similarity using N-gram Regression Models and Web Snippets
We estimate the semantic similarity between two sentences using regression models with features: 1) n-gram hit rates (lexical matches) between sentences, 2) lexical semantic similarity between non-matching words, and 3) sentence length. Lexical semantic similarity is computed via co-occurrence counts on a corpus harvested from the web using a modified mutual information metric. State-of-the-art...
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تاریخ انتشار 2013