81 citations · 343 across the 26 of their papers we have counts for
11 papers · 1 filter
Rapid Adaptation of POS Tagging for Domain Specific Uses
John E. Miller, Michael Bloodgood, Manabu Torii +1
Part-of-speech (POS) tagging is a fundamental component for performing natural language tasks such as parsing, information extraction, and question answering. When POS taggers are…
A random forest system combination approach for error detection in digital dictionaries
Michael Bloodgood, Peng Ye, Paul Rodrigues +2
When digitizing a print bilingual dictionary, whether via optical character recognition or manual entry, it is inevitable that errors are introduced into the electronic version tha…
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling
Paul Rodrigues, David Zajic, David Doermann +2
Dictionaries are often developed using tools that save to Extensible Markup Language (XML)-based standards. These standards often allow high-level repeating elements to represent l…
Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language
David Zajic, Michael Maxwell, David Doermann +2
We describe a paradigm for combining manual and automatic error correction of noisy structured lexicographic data. Modifications to the structure and underlying text of the lexicog…
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation
Michael Bloodgood, Chris Callison-Burch
We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck t…
Using Mechanical Turk to Build Machine Translation Evaluation Sets
Michael Bloodgood, Chris Callison-Burch
Building machine translation (MT) test sets is a relatively expensive task. As MT becomes increasingly desired for more and more language pairs and more and more domains, it become…
A Modality Lexicon and its use in Automatic Tagging
Kathryn Baker, Michael Bloodgood, Bonnie J. Dorr +3
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 Syntactic Machine Translation: A Tree-Grafting Approach
Kathryn Baker, Michael Bloodgood, Chris Callison-Burch +5
We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic…
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping
Michael Bloodgood, K. Vijay-Shanker
A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stabl…
Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets
Michael Bloodgood, K. Vijay-Shanker
Actively sampled data can have very different characteristics than passively sampled data. Therefore, it's promising to investigate using different inference procedures during AL t…
An Approach to Reducing Annotation Costs for BioNLP
Michael Bloodgood, K. Vijay-Shanker
There is a broad range of BioNLP tasks for which active learning (AL) can significantly reduce annotation costs and a specific AL algorithm we have developed is particularly effect…