81 citations · 343 across the 26 of their papers we have counts for
27 papers
Impact of Stop Sets on Stopping Active Learning for Text Classification
Luke Kurlandski, Michael Bloodgood
Active learning is an increasingly important branch of machine learning and a powerful technique for natural language processing. The main advantage of active learning is its poten…
Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning
Thomas Orth, Michael Bloodgood
When creating text classification systems, one of the major bottlenecks is the annotation of training data. Active learning has been proposed to address this bottleneck using stopp…
Electric Switching of the Charge-Density-Wave and Normal Metallic Phases in Tantalum Disulfide Thin-Film Devices
A. Geremew, S. Rumyantsev, F. Kargar +7
We report on switching among three charge-density-wave phases - commensurate, nearly commensurate, incommensurate - and the high-temperature normal metallic phase in thin-film 1T-T…
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification
Garrett Beatty, Ethan Kochis, Michael Bloodgood
Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training da…
Stopping Active Learning based on Predicted Change of F Measure for Text Classification
Michael Altschuler, Michael Bloodgood
During active learning, an effective stopping method allows users to limit the number of annotations, which is cost effective. In this paper, a new stopping method called Predicted…
Impact of Batch Size on Stopping Active Learning for Text Classification
Garrett Beatty, Ethan Kochis, Michael Bloodgood
When using active learning, smaller batch sizes are typically more efficient from a learning efficiency perspective. However, in practice due to speed and human annotator considera…
Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection
Michael Bloodgood
This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as informat…
Acquisition of Translation Lexicons for Historically Unwritten Languages via Bridging Loanwords
Michael Bloodgood, Benjamin Strauss
With the advent of informal electronic communications such as social media, colloquial languages that were historically unwritten are being written for the first time in heavily co…
Using Global Constraints and Reranking to Improve Cognates Detection
Michael Bloodgood, Benjamin Strauss
Global constraints and reranking have not been used in cognates detection research to date. We propose methods for using global constraints by performing rescoring of the score mat…
Filtering Tweets for Social Unrest
Alan Mishler, Kevin Wonus, Wendy Chambers +1
Since the events of the Arab Spring, there has been increased interest in using social media to anticipate social unrest. While efforts have been made toward automated unrest predi…
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection
Michael Bloodgood, Benjamin Strauss
Many important forms of data are stored digitally in XML format. Errors can occur in the textual content of the data in the fields of the XML. Fixing these errors manually is time-…
Translation Memory Retrieval Methods
Michael Bloodgood, Benjamin Strauss
Translation Memory (TM) systems are one of the most widely used translation technologies. An important part of TM systems is the matching algorithm that determines what translation…
Analysis of Stopping Active Learning based on Stabilizing Predictions
Michael Bloodgood, John Grothendieck
Within the natural language processing (NLP) community, active learning has been widely investigated and applied in order to alleviate the annotation bottleneck faced by developers…
Statistical modality tagging from rule-based annotations and crowdsourcing
Vinodkumar Prabhakaran, Michael Bloodgood, Mona Diab +5
We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tag…
Use of Modality and Negation in Semantically-Informed Syntactic MT
Kathryn Baker, Michael Bloodgood, Bonnie J. Dorr +5
This paper describes the resource- and system-building efforts of an eight-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Lang…
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…
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…