Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
Placing search in context: the concept revisited
ACM Transactions on Information Systems (TOIS)
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
The Journal of Machine Learning Research
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Text segmentation with LDA-based Fisher kernel
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Using wiktionary for computing semantic relatedness
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Wikipedia-based semantic interpretation for natural language processing
Journal of Artificial Intelligence Research
WikiWalk: random walks on Wikipedia for semantic relatedness
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Wisdom of crowds versus wisdom of linguists – measuring the semantic relatedness of words
Natural Language Engineering
Learning 5000 relational extractors
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A word at a time: computing word relatedness using temporal semantic analysis
Proceedings of the 20th international conference on World wide web
To each his own: personalized content selection based on text comprehensibility
Proceedings of the fifth ACM international conference on Web search and data mining
Combining latent factor model with location features for event-based group recommendation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approach to learning word-word relatedness, where known pairs of related words impose constraints on the learning process. We learn for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears. Our method, called CLEAR, is shown to significantly outperform previously published approaches. The proposed method is based on first principles, and is generic enough to exploit diverse types of text corpora, while having the flexibility to impose constraints on the derived word similarities. We also make publicly available a new labeled dataset for evaluating word relatedness algorithms, which we believe to be the largest such dataset to date.