Tamara Sumner


2016

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Bayesian Supervised Domain Adaptation for Short Text Similarity
Md Arafat Sultan | Jordan Boyd-Graber | Tamara Sumner
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Fast and Easy Short Answer Grading with High Accuracy
Md Arafat Sultan | Cristobal Salazar | Tamara Sumner
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity
Md Arafat Sultan | Steven Bethard | Tamara Sumner
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Feature-Rich Two-Stage Logistic Regression for Monolingual Alignment
Md Arafat Sultan | Steven Bethard | Tamara Sumner
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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SGRank: Combining Statistical and Graphical Methods to Improve the State of the Art in Unsupervised Keyphrase Extraction
Soheil Danesh | Tamara Sumner | James H. Martin
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition
Md Arafat Sultan | Steven Bethard | Tamara Sumner
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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DLS@CU: Sentence Similarity from Word Alignment
Md Arafat Sultan | Steven Bethard | Tamara Sumner
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence
Md Arafat Sultan | Steven Bethard | Tamara Sumner
Transactions of the Association for Computational Linguistics, Volume 2

We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F1 scores of 88–92%, demonstrating 1–3% absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task.

2013

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DLS@CU-CORE: A Simple Machine Learning Model of Semantic Textual Similarity
Md. Sultan | Steven Bethard | Tamara Sumner
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

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CU : Computational Assessment of Short Free Text Answers - A Tool for Evaluating Students’ Understanding
Ifeyinwa Okoye | Steven Bethard | Tamara Sumner
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Identifying science concepts and student misconceptions in an interactive essay writing tutor
Steven Bethard | Ifeyinwa Okoye | Md. Arafat Sultan | Haojie Hang | James H. Martin | Tamara Sumner
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

2008

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Extractive Summaries for Educational Science Content
Sebastian de la Chica | Faisal Ahmad | James H. Martin | Tamara Sumner
Proceedings of ACL-08: HLT, Short Papers

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Pedagogically Useful Extractive Summaries for Science Education
Sebastian de la Chica | Faisal Ahmad | James H. Martin | Tamara Sumner
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)