Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities
Scholars in inter-disciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora. Yet, under-resourced languages, imperfect or noisily structured data, and user-specific classification tasks make it difficult to meet their needs using off-the-shelf models. Manual annotation of large corpora from scratch, meanwhile, can be prohibitively expensive. Thus, we propose an active learning solution for named entity recognition, attempting to maximize a custom model{'}s improvement per additional unit of manual annotation. Our system robustly handles any domain or user-defined label set and requires no external resources, enabling quality named entity recognition for Humanities corpora where such resources are not available. Evaluating on typologically disparate languages and datasets, we reduce required annotation by 20-60{\%} and greatly outperform a competitive active learning baseline.
Code (2)
Tasks
Active LearningGeneral Classificationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Similar Papers 제목 키워드 기반
nerblackbox: A High-level Library for Named Entity Recognition in Python
We present nerblackbox, a python library to facilitate the use of state-of-the-art transformer-based models for named entity recognition. It provides simple-to-use yet powerful methods to access data and models from a wi…
named-entity-recognitionNamed Entity RecognitionRe-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition
Active learning, a widely adopted technique for enhancing machine learning models in text and image classification tasks with limited annotation resources, has received relatively little attention in the domain of Named …
Active Learningimage-classificationImage Classificationnamed-entity-recognition+3Constructing a Korean Named Entity Recognition Dataset for the Financial Domain using Active Learning
The performance of deep learning models depends on the quality and quantity of data. Data construction, however, is time- consuming and costly. In addition, when expert domain data are constructed, the availability of ex…
Active Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition
Existing deep active learning algorithms achieve impressive sampling efficiency on natural language processing tasks. However, they exhibit several weaknesses in practice, including (a) inability to use uncertainty sampl…
Active Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+1Named Entity Inclusion in Abstractive Text Summarization
We address the named entity omission - the drawback of many current abstractive text summarizers. We suggest a custom pretraining objective to enhance the model's attention on the named entities in a text. At first, the …
Abstractive Text Summarizationnamed-entity-recognitionNamed Entity RecognitionText Summarization