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Found 27 papers, 0 papers with code
Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection
This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as information extraction applications.
Impact of Batch Size on Stopping Active Learning for Text Classification
When using active learning, smaller batch sizes are typically more efficient from a learning efficiency perspective.
Acquisition of Translation Lexicons for Historically Unwritten Languages via Bridging Loanwords
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 code-switched environments.
Using Global Constraints and Reranking to Improve Cognates Detection
Global constraints and reranking have not been used in cognates detection research to date.
Filtering Tweets for Social Unrest
Since the events of the Arab Spring, there has been increased interest in using social media to anticipate social unrest.
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection
Fixing these errors manually is time-consuming and expensive, especially for large amounts of data.
Translation Memory Retrieval Methods
Although detailed accounts of the matching algorithms used in commercial systems can't be found in the literature, it is widely believed that edit distance algorithms are used.
Analysis of Stopping Active Learning based on Stabilizing Predictions
Specifically, if the Kappa agreement between two models exceeds a threshold T (where $T>0$), then the difference in F-measure performance between those models is bounded above by $\frac{4(1-T)}{T}$ in all cases.
Statistical modality tagging from rule-based annotations and crowdsourcing
We explore training an automatic modality tagger.
Use of Modality and Negation in Semantically-Informed Syntactic MT
We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of semantic annotations.
Annotating Cognates and Etymological Origin in Turkic Languages
Our method strives to balance the amount of research effort the annotator expends with the utility of the annotations for supporting research on improving automated translation lexicon induction.
Rapid Adaptation of POS Tagging for Domain Specific Uses
Part-of-speech (POS) tagging is a fundamental component for performing natural language tasks such as parsing, information extraction, and question answering.
A random forest system combination approach for error detection in digital dictionaries
We investigate combining methods and show that using random forests is a promising approach.
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling
Dictionaries are often developed using tools that save to Extensible Markup Language (XML)-based standards.
Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language
We describe a paradigm for combining manual and automatic error correction of noisy structured lexicographic data.
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation
We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources.
Using Mechanical Turk to Build Machine Translation Evaluation Sets
Building machine translation (MT) test sets is a relatively expensive task.
A Modality Lexicon and its use in Automatic Tagging
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.
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach
We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation.
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping
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 stable across changing datasets.
Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets
Actively sampled data can have very different characteristics than passively sampled data.
An Approach to Reducing Annotation Costs for BioNLP
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 effective in reducing annotation costs for these tasks.
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification
A crucial aspect of active learning is determining when to stop labeling data.
Stopping Active Learning based on Predicted Change of F Measure for Text Classification
During active learning, an effective stopping method allows users to limit the number of annotations, which is cost effective.
Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning
An important capability for improving the utility of stopping methods is to effectively forecast the performance of the text classification models.
Impact of Stop Sets on Stopping Active Learning for Text Classification
This paper shows the choice of the stop set can have a significant impact on the performance of stopping methods and the impact is different for stability-based methods from that on confidence-based methods.
