Thijs Ossenkoppele
2021
Interrater Disagreement Resolution: A Systematic Procedure to Reach Consensus in Annotation Tasks
Yvette Oortwijn
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Thijs Ossenkoppele
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Arianna Betti
Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)
We present a systematic procedure for interrater disagreement resolution. The procedure is general, but of particular use in multiple-annotator tasks geared towards ground truth construction. We motivate our proposal by arguing that, barring cases in which the researchers’ goal is to elicit different viewpoints, interrater disagreement is a sign of poor quality in the design or the description of a task. Consensus among annotators, we maintain, should be striven for, through a systematic procedure for disagreement resolution such as the one we describe.
2020
Expert Concept-Modeling Ground Truth Construction for Word Embeddings Evaluation in Concept-Focused Domains
Arianna Betti
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Martin Reynaert
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Thijs Ossenkoppele
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Yvette Oortwijn
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Andrew Salway
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Jelke Bloem
Proceedings of the 28th International Conference on Computational Linguistics
We present a novel, domain expert-controlled, replicable procedure for the construction of concept-modeling ground truths with the aim of evaluating the application of word embeddings. In particular, our method is designed to evaluate the application of word and paragraph embeddings in concept-focused textual domains, where a generic ontology does not provide enough information. We illustrate the procedure, and validate it by describing the construction of an expert ground truth, QuiNE-GT. QuiNE-GT is built to answer research questions concerning the concept of naturalized epistemology in QUINE, a 2-million-token, single-author, 20th-century English philosophy corpus of outstanding quality, cleaned up and enriched for the purpose. To the best of our ken, expert concept-modeling ground truths are extremely rare in current literature, nor has the theoretical methodology behind their construction ever been explicitly conceptualised and properly systematised. Expert-controlled concept-modeling ground truths are however essential to allow proper evaluation of word embeddings techniques, and increase their trustworthiness in specialised domains in which the detection of concepts through their expression in texts is important. We highlight challenges, requirements, and prospects for future work.
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