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Identifying science concepts and student misconceptions in an interactive essay writing tutor.

, , , , , and . BEA@NAACL-HLT, page 12-21. The Association for Computer Linguistics, (2012)978-1-937284-20-6.

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Identifying science concepts and student misconceptions in an interactive essay writing tutor., , , , , and . BEA@NAACL-HLT, page 12-21. The Association for Computer Linguistics, (2012)978-1-937284-20-6.A Joint Model for Answer Sentence Ranking and Answer Extraction., , and . TACL, (2016)Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence., , and . TACL, (2014)Towards automatic identification of core concepts in educational resources., , and . JCDL, page 379-388. IEEE Computer Society, (2014)DLS$@$CU-CORE: A Simple Machine Learning Model of Semantic Textual Similarity., , and . *SEM@NAACL-HLT, page 176-180. Association for Computational Linguistics, (2013)Feature-Rich Two-Stage Logistic Regression for Monolingual Alignment., , and . EMNLP, page 949-959. The Association for Computational Linguistics, (2015)DLS$@$CU: Sentence Similarity from Word Alignment., , and . SemEval@COLING, page 241-246. The Association for Computer Linguistics, (2014)DLS$@$CU: Sentence Similarity from Word Alignment and Semantic Vector Composition., , and . SemEval@NAACL-HLT, page 148-153. The Association for Computer Linguistics, (2015)Fast and Easy Short Answer Grading with High Accuracy., , and . HLT-NAACL, page 1070-1075. The Association for Computational Linguistics, (2016)Bayesian Supervised Domain Adaptation for Short Text Similarity., , and . HLT-NAACL, page 927-936. The Association for Computational Linguistics, (2016)