Using syntactic dependency as local context to resolve word sense ambiguity
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
A semantic approach to IE pattern induction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A unified architecture for natural language processing: deep neural networks with multitask learning
Proceedings of the 25th international conference on Machine learning
Acquiring knowledge from the web to be used as selectors for noun sense disambiguation
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Word representations: a simple and general method for semi-supervised learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
SemEval-2012 task 6: a pilot on semantic textual similarity
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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We present the Penn system for SemEval-2012 Task 6, computing the degree of semantic equivalence between two sentences. We explore the contributions of different vector models for computing sentence and word similarity: Collobert and Weston embeddings as well as two novel approaches, namely eigen-words and selectors. These embeddings provide different measures of distributional similarity between words, and their contexts. We used regression to combine the different similarity measures, and found that each provides partially independent predictive signal above baseline models.