Shane Bergsma


2014

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I’m a Belieber: Social Roles via Self-identification and Conceptual Attributes
Charley Beller | Rebecca Knowles | Craig Harman | Shane Bergsma | Margaret Mitchell | Benjamin Van Durme
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Learning Domain-Specific, L1-Specific Measures of Word Readability
Shane Bergsma | David Yarowsky
Traitement Automatique des Langues, Volume 54, Numéro 1 : Varia [Varia]

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Broadly Improving User Classification via Communication-Based Name and Location Clustering on Twitter
Shane Bergsma | Mark Dredze | Benjamin Van Durme | Theresa Wilson | David Yarowsky
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Using Conceptual Class Attributes to Characterize Social Media Users
Shane Bergsma | Benjamin Van Durme
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Explicit and Implicit Syntactic Features for Text Classification
Matt Post | Shane Bergsma
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Stylometric Analysis of Scientific Articles
Shane Bergsma | Matt Post | David Yarowsky
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Language Identification for Creating Language-Specific Twitter Collections
Shane Bergsma | Paul McNamee | Mossaab Bagdouri | Clayton Fink | Theresa Wilson
Proceedings of the Second Workshop on Language in Social Media

2011

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Using Visual Information to Predict Lexical Preference
Shane Bergsma | Randy Goebel
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

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Using Large Monolingual and Bilingual Corpora to Improve Coordination Disambiguation
Shane Bergsma | David Yarowsky | Kenneth Church
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Joint Training of Dependency Parsing Filters through Latent Support Vector Machines
Colin Cherry | Shane Bergsma
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Creating Robust Supervised Classifiers via Web-Scale N-Gram Data
Shane Bergsma | Emily Pitler | Dekang Lin
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Predicting the Semantic Compositionality of Prefix Verbs
Shane Bergsma | Aditya Bhargava | Hua He | Grzegorz Kondrak
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Fast and Accurate Arc Filtering for Dependency Parsing
Shane Bergsma | Colin Cherry
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Using Web-scale N-grams to Improve Base NP Parsing Performance
Emily Pitler | Shane Bergsma | Dekang Lin | Kenneth Church
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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New Tools for Web-Scale N-grams
Dekang Lin | Kenneth Church | Heng Ji | Satoshi Sekine | David Yarowsky | Shane Bergsma | Kailash Patil | Emily Pitler | Rachel Lathbury | Vikram Rao | Kapil Dalwani | Sushant Narsale
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

While the web provides a fantastic linguistic resource, collecting and processing data at web-scale is beyond the reach of most academic laboratories. Previous research has relied on search engines to collect online information, but this is hopelessly inefficient for building large-scale linguistic resources, such as lists of named-entity types or clusters of distributionally similar words. An alternative to processing web-scale text directly is to use the information provided in an N-gram corpus. An N-gram corpus is an efficient compression of large amounts of text. An N-gram corpus states how often each sequence of words (up to length N) occurs. We propose tools for working with enhanced web-scale N-gram corpora that include richer levels of source annotation, such as part-of-speech tags. We describe a new set of search tools that make use of these tags, and collectively lower the barrier for lexical learning and ambiguity resolution at web-scale. They will allow novel sources of information to be applied to long-standing natural language challenges.

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Transliteration Generation and Mining with Limited Training Resources
Sittichai Jiampojamarn | Kenneth Dwyer | Shane Bergsma | Aditya Bhargava | Qing Dou | Mi-Young Kim | Grzegorz Kondrak
Proceedings of the 2010 Named Entities Workshop

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Improved Natural Language Learning via Variance-Regularization Support Vector Machines
Shane Bergsma | Dekang Lin | Dale Schuurmans
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

2009

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Glen, Glenda or Glendale: Unsupervised and Semi-supervised Learning of English Noun Gender
Shane Bergsma | Dekang Lin | Randy Goebel
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

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A Ranking Approach to Stress Prediction for Letter-to-Phoneme Conversion
Qing Dou | Shane Bergsma | Sittichai Jiampojamarn | Grzegorz Kondrak
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

2008

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Distributional Identification of Non-Referential Pronouns
Shane Bergsma | Dekang Lin | Randy Goebel
Proceedings of ACL-08: HLT

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Discriminative Learning of Selectional Preference from Unlabeled Text
Shane Bergsma | Dekang Lin | Randy Goebel
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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Automatic Answer Typing for How-Questions
Christopher Pinchak | Shane Bergsma
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Alignment-Based Discriminative String Similarity
Shane Bergsma | Grzegorz Kondrak
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Learning Noun Phrase Query Segmentation
Shane Bergsma | Qin Iris Wang
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Bootstrapping Path-Based Pronoun Resolution
Shane Bergsma | Dekang Lin
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

2005

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An Expectation Maximization Approach to Pronoun Resolution
Colin Cherry | Shane Bergsma
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)