2017
EMNLP
EMNLP 2017
Exploring Vector Spaces for Semantic Relations
Abstract
AbstractWord embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods and just cosine similarity, encouraging results were obtained for analogical similarities. In this paper, we explore the potential of pre-trained word embeddings to identify generic types of semantic relations in an unsupervised experiment. We propose a new relational similarity measure based on the combination of word2vec’s CBOW input and output vectors which outperforms concurrent vector representations, when used for unsupervised clustering on SemEval 2010 Relation Classification data.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— relational similarity
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Hot Topic Early Bird
— semantic relation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Metric Learning
Machine Learning > Core Methods > Embedding Learning
Machine Learning > Learning Types > Unsupervised Learning
Natural Language Processing > Resources & Methods > Text Representation
Deep Learning > Learning Types > Unsupervised Learning
Artificial Intelligence > Core AI > Natural Language Processing