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_data/allamanistaxonomy.yml

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- {bibkey: zaremba2014learning, categories: [representational], input_rep: Characters , modeled_target: Execution Trace , intermediate_rep: Distributed , application: --- }
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# Pattern Models
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- {bibkey: allamanis2016mining, categories: [pattern], representation: Abstracted AST , pattern_type: Graphical Model , application: Semantic Idiom Mining }
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- {bibkey: allamanis2017mining, categories: [pattern], representation: Abstracted AST , pattern_type: Graphical Model , application: Semantic Idiom Mining }
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- {bibkey: fowkes2016parameter, categories: [pattern], representation: API Call Sequences , pattern_type: Graphical Model , application: API Mining }
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- {bibkey: murali2017bayesian, categories: [pattern], representation: Sketch Synthesis , pattern_type: Graphical Model , application: Sketch Mining }
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- {bibkey: murali2017finding, categories: [pattern], representation: API Usage Errors , pattern_type: Graphical Model , application: Defect Prediction }

_publications/movshovitz2015kblda.markdown renamed to _publications/movshovitz2015kb.markdown

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@@ -4,7 +4,7 @@ title: "KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and F
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authors: D. Movshovitz-Attias, W. W. Cohen
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conference: ACL
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year: 2015
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bibkey: movshovitz2015kblda
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bibkey: movshovitz2015kb
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---
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Many existing knowledge bases (KBs), including Freebase, Yago, and NELL, rely
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on a fixed ontology, given as an input

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