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  <title><![CDATA[Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles]]></title>
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  <description><![CDATA[We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that consistently encodes syntactic-semantic content better, with multiple 10% gains over baselines.]]></description>
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  <title><![CDATA[Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity systems]]></title>
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  <description><![CDATA[We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TF-IDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference ...]]></description>
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