Christine Piatko

Also published as: Christine D. Piatko


pdf bib
Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT
Kathryn Baker | Michael Bloodgood | Bonnie J. Dorr | Chris Callison-Burch | Nathaniel W. Filardo | Christine Piatko | Lori Levin | Scott Miller
Computational Linguistics, Volume 38, Issue 2 - June 2012

pdf bib
Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing
Vinodkumar Prabhakaran | Michael Bloodgood | Mona Diab | Bonnie Dorr | Lori Levin | Christine D. Piatko | Owen Rambow | Benjamin Van Durme
Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics


pdf bib
A Modality Lexicon and its use in Automatic Tagging
Kathryn Baker | Michael Bloodgood | Bonnie Dorr | Nathaniel W. Filardo | Lori Levin | Christine Piatko
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper describes our resource-building results for an eight-week JHU Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation. Specifically, we describe the construction of a modality annotation scheme, a modality lexicon, and two automated modality taggers that were built using the lexicon and annotation scheme. Our annotation scheme is based on identifying three components of modality: a trigger, a target and a holder. We describe how our modality lexicon was produced semi-automatically, expanding from an initial hand-selected list of modality trigger words and phrases. The resulting expanded modality lexicon is being made publicly available. We demonstrate that one tagger―a structure-based tagger―results in precision around 86% (depending on genre) for tagging of a standard LDC data set. In a machine translation application, using the structure-based tagger to annotate English modalities on an English-Urdu training corpus improved the translation quality score for Urdu by 0.3 Bleu points in the face of sparse training data.

pdf bib
We’re Not in Kansas Anymore: Detecting Domain Changes in Streams
Mark Dredze | Tim Oates | Christine Piatko
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing


pdf bib
Arabic Cross-Document Coreference Resolution
Asad Sayeed | Tamer Elsayed | Nikesh Garera | David Alexander | Tan Xu | Doug Oard | David Yarowsky | Christine Piatko
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers


pdf bib
Using “Annotator Rationales” to Improve Machine Learning for Text Categorization
Omar Zaidan | Jason Eisner | Christine Piatko
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference


pdf bib
Named Entity Recognition using Hundreds of Thousands of Features
James Mayfield | Paul McNamee | Christine Piatko
Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003