Data Centric Domain Adaptation for Historical Text with OCR Errors
We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French. For the cross-domain case, we address domain shift by integrating unsupervised in-domain data via contextualized string embeddings; and OCR errors by injecting synthetic OCR errors into the source domain and address data centric domain adaptation. We propose a general approach to imitate OCR errors in arbitrary input data. Our cross-domain as well as our in-domain results outperform several strong baselines and establish state-of-the-art results. We publish preprocessed versions of the French and Dutch Europeana NER corpora.
Code (1)
Tasks
Cross-Domain Named Entity RecognitionDomain Adaptationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NEROptical Character Recognition (OCR)Similar Papers 제목 키워드 기반
Domain Adaptation and Reasoning Frameworks in Language Models: A Controlled Experiment with Historical Cosmology
We investigate how domain adaptation reshapes explanatory behavior in language models using historical cosmology as a controlled setting. In Phase 1, we train a small language model from scratch on a pre-Copernican corpu…
Domain AdaptationPart-of-Speech Tagging for Historical English
As more historical texts are digitized, there is interest in applying natural language processing tools to these archives. However, the performance of these tools is often unsatisfactory, due to language change and genre…
Domain AdaptationPart-Of-Speech TaggingUnsupervised Domain AdaptationWord EmbeddingsAggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation
Unsupervised graph domain adaptation (UGDA) focuses on transferring knowledge from labeled source graph to unlabeled target graph under domain discrepancies. Most existing UGDA methods are designed to adapt information f…
Domain AdaptationGRAPH DOMAIN ADAPTATIONEgo-VPA: Egocentric Video Understanding with Parameter-efficient Adaptation
Video understanding typically requires fine-tuning the large backbone when adapting to new domains. In this paper, we leverage the egocentric video foundation models (Ego-VFMs) based on video-language pre-training and pr…
Video UnderstandingModel Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled target domain, but it requires to access the source data which often raises concerns in data privacy, data portability and data transm…
Contrastive LearningDomain AdaptationSource Free Object DetectionUnsupervised Domain Adaptation