Matthew Snover

Also published as: Matthew G. Snover


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Cross-lingual Slot Filling from Comparable Corpora
Matthew Snover | Xiang Li | Wen-Pin Lin | Zheng Chen | Suzanne Tamang | Mingmin Ge | Adam Lee | Qi Li | Hao Li | Sam Anzaroot | Heng Ji
Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web

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Unsupervised Language-Independent Name Translation Mining from Wikipedia Infoboxes
Wen-Pin Lin | Matthew Snover | Heng Ji
Proceedings of the First workshop on Unsupervised Learning in NLP


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Fluency, Adequacy, or HTER? Exploring Different Human Judgments with a Tunable MT Metric
Matthew Snover | Nitin Madnani | Bonnie Dorr | Richard Schwartz
Proceedings of the Fourth Workshop on Statistical Machine Translation


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Language and Translation Model Adaptation using Comparable Corpora
Matthew Snover | Bonnie Dorr | Richard Schwartz
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing


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PCFGs with Syntactic and Prosodic Indicators of Speech Repairs
John Hale | Izhak Shafran | Lisa Yung | Bonnie J. Dorr | Mary Harper | Anna Krasnyanskaya | Matthew Lease | Yang Liu | Brian Roark | Matthew Snover | Robin Stewart
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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SParseval: Evaluation Metrics for Parsing Speech
Brian Roark | Mary Harper | Eugene Charniak | Bonnie Dorr | Mark Johnson | Jeremy Kahn | Yang Liu | Mari Ostendorf | John Hale | Anna Krasnyanskaya | Matthew Lease | Izhak Shafran | Matthew Snover | Robin Stewart | Lisa Yung
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

While both spoken and written language processing stand to benefit from parsing, the standard Parseval metrics (Black et al., 1991) and their canonical implementation (Sekine and Collins, 1997) are only useful for text. The Parseval metrics are undefined when the words input to the parser do not match the words in the gold standard parse tree exactly, and word errors are unavoidable with automatic speech recognition (ASR) systems. To fill this gap, we have developed a publicly available tool for scoring parses that implements a variety of metrics which can handle mismatches in words and segmentations, including: alignment-based bracket evaluation, alignment-based dependency evaluation, and a dependency evaluation that does not require alignment. We describe the different metrics, how to use the tool, and the outcome of an extensive set of experiments on the sensitivity.


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A Lexically-Driven Algorithm for Disfluency Detection
Matthew Snover | Bonnie Dorr | Richard Schwartz
Proceedings of HLT-NAACL 2004: Short Papers


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Unsupervised Learning of Morphology Using a Novel Directed Search Algorithm: Taking the First Step
Matthew G. Snover | Gaja E. Jarosz | Michael R. Brent
Proceedings of the ACL-02 Workshop on Morphological and Phonological Learning


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A Bayesian Model For Morpheme and Paradigm Identification
Matthew G. Snover | Michael R. Brent
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics