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Found 7 papers, 1 papers with code
How Well Do Embedding Models Capture Non-compositionality? A View from Multiword Expressions
In this paper, we apply various embedding methods on multiword expressions to study how well they capture the nuances of non-compositional data.
A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions
In this paper, we perform a comparative evaluation of off-the-shelf embedding models over the task of compositionality prediction of multiword expressions(``MWEs'').
IIT-UHH at SemEval-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification
In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval-2017 Task 3.
Semi-supervised Clustering of Medical Text
Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications.
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