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Found 11 papers, 1 papers with code
Towards interpretable, data-derived distributional meaning representations for reasoning: A dataset of properties and concepts
We provide hypotheses on which properties are reflected in distributional data or not based on the type of relation.
Story Trees: Representing Documents using Topological Persistence
Topological Data Analysis (TDA) focuses on the inherent shape of (spatial) data.
ARN: Analogical Reasoning on Narratives
However, while cognitive theories of analogy often focus on narratives and study the distinction between surface, relational, and system similarities, existing work in natural language processing has a narrower focus as far as relational analogies between word pairs.
The Role of Interactive Visualization in Explaining (Large) NLP Models: from Data to Inference
We motivate the use of visualization in relation to target users and common NLP pipelines.
Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projection
Applying one targeted (MP) projection hence is methodologically cleaner than applying multiple (INLP) projections that introduce random effects.
Challenging distributional models with a conceptual network of philosophical terms
We investigate the possibilities and limitations of using distributional semantic models for analyzing philosophical data by means of a realistic use-case.
Would you describe a leopard as yellow? Evaluating crowd-annotations with justified and informative disagreement
We establish an additional, agreement-independent quality metric based on answer-coherence and evaluate it in comparison to existing metrics.
Why is penguin more similar to polar bear than to sea gull? Analyzing conceptual knowledge in distributional models
The expected outcome is a better understanding of (1) the semantic information we can infer purely based on linguistic co-occurrence patterns and (2) the potential of distributional semantic models to pick up linguistic evidence.
Conceptual Change and Distributional Semantic Models: an Exploratory Study on Pitfalls and Possibilities
Studying conceptual change using embedding models has become increasingly popular in the Digital Humanities community while critical observations about them have received less attention.
Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell
The idea behind this method is that properties identified by classifiers, but not through full vector comparison are captured by embeddings.
Meaning\_space at SemEval-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddings
This paper presents the two systems submitted by the meaning space team in Task 10 of the SemEval competition 2018 entitled Capturing discriminative attributes.
