Unsupervised Common Sense Relation Extraction
Vast and diverse knowledge about the relations in the world help humans comprehend and argue about their environment. Equipping machines with this knowledge is challenging yet essential for general reasoning capabilities. Here, we propose to apply unsupervised relation extraction (URE), aiming to induce general relations between concepts from natural language. Previous work in URE has predominantly focused on relations between named entities in the encyclopedic domain. The more general, and more challenging, domain of common sense relation learning has not yet been addressed, partially due to a lack of datasets. We present a framework for common sense relation extraction from free-text, associated with two benchmark datasets. We present initial experiments using three state-of-the-art models developed for encyclopedic relation induction. Our results verify the utility of our benchmarks for common sense relation extraction, and suggest ample scope for future work on this important, yet challenging, task.
Code (0)
등록된 구현이 없습니다.
Tasks
Common Sense ReasoningRelationRelation ExtractionSimilar Papers 제목 키워드 기반
Unsupervised Sense-Aware Hypernymy Extraction
In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of syn…
Understanding Substructures in Commonsense Relations in ConceptNet
Acquiring commonsense knowledge and reasoning is an important goal in modern NLP research. Despite much progress, there is still a lack of understanding (especially at scale) of the nature of commonsense knowledge itself…
Graph Representation LearningRepresentation LearningAutomatic Extraction of Commonsense LocatedNear Knowledge
LocatedNear relation is a kind of commonsense knowledge describing two physical objects that are typically found near each other in real life. In this paper, we study how to automatically extract such relationship throug…
RelationSentenceAffordance Extraction and Inference based on Semantic Role Labeling
Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain which aspects of 'coffee' and 'tea' make …
Common Sense ReasoningSemantic Role LabelingWord EmbeddingsWord SimilarityVisually Grounded Commonsense Knowledge Acquisition
Large-scale commonsense knowledge bases empower a broad range of AI applications, where the automatic extraction of commonsense knowledge (CKE) is a fundamental and challenging problem. CKE from text is known for sufferi…
Language Modelling