Search Results for author:
Found 43 papers, 15 papers with code
The Predicate Matrix and the Event and Implied Situation Ontology: Making More of Events
This paper presents the Event and Implied Situation Ontology (ESO), a resource which formalizes the pre and post situations of events and the roles of the entities affected by an event.
ReferenceNet: a semantic-pragmatic network for capturing reference relations.
In this paper, we present ReferenceNet: a semantic-pragmatic network of reference relations between synsets.
Numeracy enhances the Literacy of Language Models
This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy.
Case-Based Reasoning with Language Models for Classification of Logical Fallacies
The ease and the speed of spreading misinformation and propaganda on the Web motivate the need to develop trustworthy technology for detecting fallacies in natural language arguments.
Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
Our three-stage framework natively consolidates prior datasets and methods from existing tasks, like propaganda detection, serving as an overarching evaluation testbed.
Multimodal and Explainable Internet Meme Classification
In this paper, we pursue a modular and explainable architecture for Internet meme understanding.
A Study of Slang Representation Methods
Considering the large amount of content created online by the minute, slang-aware automatic tools are critically needed to promote social good, and assist policymakers and moderators in restricting the spread of offensive language, abuse, and hate speech.
Utilizing Background Knowledge for Robust Reasoning over Traffic Situations
Understanding novel situations in the traffic domain requires an intricate combination of domain-specific and causal commonsense knowledge.
PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters.
Does Wikidata Support Analogical Reasoning?
Analogical reasoning methods have been built over various resources, including commonsense knowledge bases, lexical resources, language models, or their combination.
Coalescing Global and Local Information for Procedural Text Understanding
In this paper, we propose Coalescing Global and Local Information (CGLI), a new model that builds entity- and timestep-aware input representations (local input) considering the whole context (global input), and we jointly model the entity states with a structured prediction objective (global output).
Enriching Wikidata with Linked Open Data
We present a novel workflow that includes gap detection, source selection, schema alignment, and semantic validation.
Understanding Narratives through Dimensions of Analogy
Experiments with language models and neuro-symbolic AI reasoners on these tasks reveal that state-of-the-art methods can be applied to reason by analogy with a limited success, motivating the need for further research towards comprehensive and scalable analogical reasoning by AI.
An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs
In this paper, we study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
Augmenting Knowledge Graphs for Better Link Prediction
Experiments on legacy benchmarks and a new large benchmark, DWD, show that augmenting the knowledge graph with quantities and years is beneficial for predicting both entities and numbers, as KGA outperforms the vanilla models and other relevant baselines.
Generalizable Neuro-symbolic Systems for Commonsense Question Answering
This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks.
Contextualized Scene Imagination for Generative Commonsense Reasoning
In this paper, we propose an Imagine-and-Verbalize (I&V) method, which learns to imagine a relational scene knowledge graph (SKG) with relations between the input concepts, and leverage the SKG as a constraint when generating a plausible scene description.
Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models
In this paper, we investigate what models learn from commonsense reasoning datasets.
Story Generation with Commonsense Knowledge Graphs and Axioms
Our method aligns story types with commonsense axioms, and queries to a commonsense knowledge graph, enabling the generation of hundreds of thousands of stories.
Analyzing Race and Country of Citizenship Bias in Wikidata
Through this analysis, we discovered that there is an overrepresentation of white individuals and those with citizenship in Europe and North America; the rest of the groups are generally underrepresented.
User-friendly Comparison of Similarity Algorithms on Wikidata
While the similarity between two concept words has been evaluated and studied for decades, much less attention has been devoted to algorithms that can compute the similarity of nodes in very large knowledge graphs, like Wikidata.
Creating and Querying Personalized Versions of Wikidata on a Laptop
Application developers today have three choices for exploiting the knowledge present in Wikidata: they can download the Wikidata dumps in JSON or RDF format, they can use the Wikidata API to get data about individual entities, or they can use the Wikidata SPARQL endpoint.
A Study of the Quality of Wikidata
Wikidata has been increasingly adopted by many communities for a wide variety of applications, which demand high-quality knowledge to deliver successful results.
Do Language Models Perform Generalizable Commonsense Inference?
Inspired by evidence that pretrained language models (LMs) encode commonsense knowledge, recent work has applied LMs to automatically populate commonsense knowledge graphs (CKGs).
PaCo: Preconditions Attributed to Commonsense Knowledge
To address this gap, we propose a novel challenge of reasoning with circumstantial preconditions.
Representing Numbers in NLP: a Survey and a Vision
NLP systems rarely give special consideration to numbers found in text.
Dimensions of Commonsense Knowledge
Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization.
CSKG: The CommonSense Knowledge Graph
Sources of commonsense knowledge support applications in natural language understanding, computer vision, and knowledge graphs.
Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering
Guided by a set of hypotheses, the framework studies how to transform various pre-existing knowledge resources into a form that is most effective for pre-training models.
Commonsense Knowledge in Wikidata
Our experiments reveal that: 1) albeit Wikidata-CS represents a small portion of Wikidata, it is an indicator that Wikidata contains relevant commonsense knowledge, which can be mapped to 15 ConceptNet relations; 2) the overlap between Wikidata-CS and other commonsense sources is low, motivating the value of knowledge integration; 3) Wikidata-CS has been evolving over time at a slightly slower rate compared to the overall Wikidata, indicating a possible lack of focus on commonsense knowledge.
Consolidating Commonsense Knowledge
Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities.
KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis
Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications.
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering
In this paper, we augment a general commonsense QA framework with a knowledgeable path generator.
Large-scale Cross-lingual Language Resources for Referencing and Framing
In this article, we lay out the basic ideas and principles of the project Framing Situations in the Dutch Language.
Combining Conceptual and Referential Annotation to Study Variation in Framing
The user can apply two types of annotations: 1) mappings from expressions to frames and frame elements, 2) reference relations from mentions to events and participants of the structured data.
The Profiling Machine: Active Generalization over Knowledge
The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks.
Systematic Study of Long Tail Phenomena in Entity Linking
In this paper we report on a series of hypotheses regarding the long tail phenomena in entity linking datasets, their interaction, and their impact on system performance.
SemEval-2018 Task 5: Counting Events and Participants in the Long Tail
This paper discusses SemEval-2018 Task 5: a referential quantification task of counting events and participants in local, long-tail news documents with high ambiguity.
Semantic overfitting: what `world' do we consider when evaluating disambiguation of text?
Semantic text processing faces the challenge of defining the relation between lexical expressions and the world to which they make reference within a period of time.
Context-enhanced Adaptive Entity Linking
More and more knowledge bases are publicly available as linked data.
Evaluating Entity Linking: An Analysis of Current Benchmark Datasets and a Roadmap for Doing a Better Job
Entity linking has become a popular task in both natural language processing and semantic web communities.
