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Found 19 papers, 4 papers with code
Distributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functional Distributional Semantics
Functional Distributional Semantics (FDS) models the meaning of words by truth-conditional functions.
Using dependency parsing for few-shot learning in distributional semantics
In this work, we explore the novel idea of employing dependency parsing information in the context of few-shot learning, the task of learning the meaning of a rare word based on a limited amount of context sentences.
Visual Spatial Reasoning
Spatial relations are a basic part of human cognition.
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Visual Reasoning
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Learning Functional Distributional Semantics with Visual Data
In this work, we propose a method to train a Functional Distributional Semantics model with grounded visual data.
Incremental Beam Manipulation for Natural Language Generation
For this reason, it is common to rerank the output of beam search, but this relies on beam search to produce a good set of hypotheses, which limits the potential gains.
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
Across languages, multiple consecutive adjectives modifying a noun (e. g. "the big red dog") follow certain unmarked ordering rules.
Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics
Functional Distributional Semantics provides a computationally tractable framework for learning truth-conditional semantics from a corpus.
Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics
Functional Distributional Semantics provides a linguistically interpretable framework for distributional semantics, by representing the meaning of a word as a function (a binary classifier), instead of a vector.
What are the Goals of Distributional Semantics?
Distributional semantic models have become a mainstay in NLP, providing useful features for downstream tasks.
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models
Word embeddings are an essential component in a wide range of natural language processing applications.
Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting
We propose a method for natural language generation, choosing the most representative output rather than the most likely output.
Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs
Distributional Semantic Models (DSMs) construct vector representations of word meanings based on their contexts.
Semantic Composition via Probabilistic Model Theory
Semantic composition remains an open problem for vector space models of semantics.
Variational Inference for Logical Inference
Functional Distributional Semantics is a framework that aims to learn, from text, semantic representations which can be interpreted in terms of truth.
Functional Distributional Semantics
Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena.
Resources for building applications with Dependency Minimal Recursion Semantics
We describe resources aimed at increasing the usability of the semantic representations utilized within the DELPH-IN (Deep Linguistic Processing with HPSG) consortium.
