Summary of An Experimental Study on Data Augmentation Techniques For Named Entity Recognition on Low-resource Domains, by Arthur Elwing Torres et al.
An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains
by Arthur Elwing Torres, Edleno Silva de Moura, Altigran Soares da Silva, Mario A. Nascimento, Filipe Mesquita
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of two text augmentation techniques, Mention Replacement and Contextual Word Replacement, on two prominent Named Entity Recognition (NER) models, Bi-LSTM+CRF and BERT. The goal is to evaluate the effectiveness of these techniques in generating additional training instances for NER tasks in low-resource domains, such as medical, legal, and financial sectors. The study conducts experiments on four datasets from these domains and investigates the impact of various combinations of training subset sizes and number of augmented examples. The results confirm that data augmentation is particularly beneficial for smaller datasets and demonstrate that there is no universally optimal number of augmented examples, emphasizing the importance of experimentation in NER project development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how to make machine learning models better at finding important words (like names and places) in texts. It’s hard to find enough training data for this task, especially when the topic is specialized like medicine or law. To solve this problem, scientists are using techniques that generate extra training examples from the original dataset. In this study, they test two of these techniques on two different models and look at how well they work on four datasets from low-resource domains. They found that making more training examples can really help with smaller datasets, but there’s no one-size-fits-all solution – each project needs to be fine-tuned. |
Keywords
» Artificial intelligence » Bert » Data augmentation » Lstm » Machine learning » Named entity recognition » Ner
