A word embedding is a distributed representation of meaning where each word is represented as a vector in ℝ n . Such representations allow a model to share meaning between similar words, and have been used to capture semantic, syntactic and morphological content [6, 25, inter alia] .
2014. Semantic Frame Identification with Distributed Word Representations. In Proceedings of the 52nd Annual Meeting of the Association for Computational ...
We present a novel technique for semantic frame identification using distributed representations of predicates and their syntactic context.
We present a novel technique for semantic frame identification using distributed representations of predicates and their syntactic context; this technique ...
We present a novel technique for semantic frame identification using distributed representations of predicates and their syntactic context; this technique ...
ABSTRACT · A computer-implemented technique can include receiving, at a server, labeled training data including a plurality of groups of words, each group of ...
Full frame-semantic analysis. In addition to the frame identification experiments, we also use our models as part of a full frame-semantic parsing setup ...
Semantic Frame Identification with Distributed Word ... - dblp
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Bibliographic details on Semantic Frame Identification with Distributed Word Representations.
Semantic Frame Identification with Distributed Word Representations. Karl Moritz Hermann‚ Dipanjan Das‚ Jason Weston and Kuzman Ganchev. Book Title.
Feb 1, 2021 · These distributed representations which are trained on the context of words turn out to be pretty good proxies for the actual meaning of a word.
Missing: Frame Identification