Summary of Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition, by Chang Tian et al.
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition
by Chang Tian, Wenpeng Yin, Dan Li, Marie-Francine Moens
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed pipeline for few-shot named entity recognition (NER) tackles challenges in current span detectors and metric-based entity-type classifiers. The improved pipeline introduces a pre-trained steppingstone span detector initialized with open-domain Wikipedia data, reducing repetitive training of basic features. It also leverages a large language model to set reliable entity-type referents, eliminating reliance on few-shot samples of each type. Experimental results demonstrate superior performance with fewer training steps and human-labeled data compared to baselines, including ChatGPT, particularly in fine-grained few-shot NER settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves the way computers recognize named entities in text using just a few examples. It solves problems with current methods by teaching the computer to recognize patterns first, then use that knowledge to identify specific types of entities. This makes it better at recognizing entities when it only has a little information to work with. |
Keywords
» Artificial intelligence » Few shot » Large language model » Named entity recognition » Ner
