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Found 11 papers, 4 papers with code
Causal datasheet: An approximate guide to practically assess Bayesian networks in the real world
Here we propose a causal extension to the datasheet concept proposed by Gebru et al (2018) to include approximate BN performance expectations for any given dataset.
Improving Semantic Composition with Offset Inference
Count-based distributional semantic models suffer from sparsity due to unobserved but plausible co-occurrences in any text collection.
When a Red Herring in Not a Red Herring: Using Compositional Methods to Detect Non-Compositional Phrases
Non-compositional phrases such as \textit{red herring} and weakly compositional phrases such as \textit{spelling bee} are an integral part of natural language (Sag, 2002).
One Representation per Word - Does it make Sense for Composition?
In this paper, we investigate whether an a priori disambiguation of word senses is strictly necessary or whether the meaning of a word in context can be disambiguated through composition alone.
Aligning Packed Dependency Trees: a theory of composition for distributional semantics
We present a new framework for compositional distributional semantics in which the distributional contexts of lexemes are expressed in terms of anchored packed dependency trees.
Improving Sparse Word Representations with Distributional Inference for Semantic Composition
Distributional models are derived from co-occurrences in a corpus, where only a small proportion of all possible plausible co-occurrences will be observed.
