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Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types
Annotated corpora enable supervised machine learning and data analysis.
Fast Inference for Interactive Models of Text
Probabilistic models are a useful means for analyzing large text corpora.
Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings
In modern text annotation projects, crowdsourced annotations are often aggregated using item response models or by majority vote.
Using Transfer Learning to Assist Exploratory Corpus Annotation
We describe an under-studied problem in language resource management: that of providing automatic assistance to annotators working in exploratory settings.
Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage
The task of corpus-dictionary linkage (CDL) is to annotate each word in a corpus with a link to an appropriate dictionary entry that documents the sense and usage of the word.
Momresp: A Bayesian Model for Multi-Annotator Document Labeling
We introduce MomResp, a model that incorporates information from both natural data clusters as well as annotations from multiple annotators to infer ground-truth labels and annotator reliability for the document classification task.
First Results in a Study Evaluating Pre-annotation and Correction Propagation for Machine-Assisted Syriac Morphological Analysis
Manual annotation of large textual corpora can be cost-prohibitive, especially for rare and under-resourced languages.
