62 citations · 122 across the 11 of their papers we have counts for
8 papers · 1 filter
Annotating Cognates and Etymological Origin in Turkic Languages
Benjamin S. Mericli, Michael Bloodgood
Turkic languages exhibit extensive and diverse etymological relationships among lexical items. These relationships make the Turkic languages promising for exploring automated trans…
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…
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…