Michael Bloodgood | DeepAI

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∙ 01/08/2022

Impact of Stop Sets on Stopping Active Learning for Text Classification

Active learning is an increasingly important branch of machine learning ...

0 Luke Kurlandski, et al. ∙

∙ 01/20/2020

Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning

When creating text classification systems, one of the major bottlenecks ...

0 Thomas Orth, et al. ∙

∙ 01/26/2019

The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification

Annotation of training data is the major bottleneck in the creation of t...

0 Garrett Beatty, et al. ∙

∙ 01/26/2019

Stopping Active Learning based on Predicted Change of F Measure for Text Classification

During active learning, an effective stopping method allows users to lim...

0 Michael Altschuler, et al. ∙

∙ 01/24/2018

Impact of Batch Size on Stopping Active Learning for Text Classification

When using active learning, smaller batch sizes are typically more effic...

0 Garrett Beatty, et al. ∙

∙ 01/24/2018

Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection

This paper investigates and evaluates support vector machine active lear...

0 Michael Bloodgood, et al. ∙

∙ 06/06/2017

Acquisition of Translation Lexicons for Historically Unwritten Languages via Bridging Loanwords

With the advent of informal electronic communications such as social med...

0 Michael Bloodgood, et al. ∙

∙ 04/24/2017

Using Global Constraints and Reranking to Improve Cognates Detection

Global constraints and reranking have not been used in cognates detectio...

0 Michael Bloodgood, et al. ∙

∙ 02/20/2017

Filtering Tweets for Social Unrest

Since the events of the Arab Spring, there has been increased interest i...

0 Alan Mishler, et al. ∙

∙ 02/25/2016

Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection

Many important forms of data are stored digitally in XML format. Errors ...

0 Michael Bloodgood, et al. ∙

∙ 04/23/2015

Analysis of Stopping Active Learning based on Stabilizing Predictions

Within the natural language processing (NLP) community, active learning ...

0 Michael Bloodgood, et al. ∙

∙ 03/04/2015

Statistical modality tagging from rule-based annotations and crowdsourcing

We explore training an automatic modality tagger. Modality is the attitu...

0 Vinodkumar Prabhakaran, et al. ∙

∙ 02/05/2015

Use of Modality and Negation in Semantically-Informed Syntactic MT

This paper describes the resource- and system-building efforts of an eig...

0 Kathryn Baker, et al. ∙

∙ 01/13/2015

Annotating Cognates and Etymological Origin in Turkic Languages

Turkic languages exhibit extensive and diverse etymological relationship...

0 Benjamin S. Mericli, et al. ∙

∙ 10/31/2014

Rapid Adaptation of POS Tagging for Domain Specific Uses

Part-of-speech (POS) tagging is a fundamental component for performing n...

0 John E. Miller, et al. ∙

∙ 10/30/2014

A random forest system combination approach for error detection in digital dictionaries

When digitizing a print bilingual dictionary, whether via optical charac...

0 Michael Bloodgood, et al. ∙

∙ 10/21/2014

Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation

We explore how to improve machine translation systems by adding more tra...

0 Michael Bloodgood, et al. ∙

∙ 10/20/2014

Using Mechanical Turk to Build Machine Translation Evaluation Sets

Building machine translation (MT) test sets is a relatively expensive ta...

0 Michael Bloodgood, et al. ∙

∙ 09/24/2014

Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach

We describe a unified and coherent syntactic framework for supporting a ...

0 Kathryn Baker, et al. ∙