Summary of Find Parent Then Label Children: a Two-stage Taxonomy Completion Method with Pre-trained Language Model, by Fei Xia et al.
Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model
by Fei Xia, Yixuan Weng, Shizhu He, Kang Liu, Jun Zhao
First submitted to arxiv on: 25 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 This paper proposes a novel method called ATTEMPT for completing taxonomies in knowledge systems. The goal is to continuously update taxonomies as domain knowledge evolves by inserting new concepts into the correct position. Unlike previous approaches that mainly focus on adding concepts to leaf nodes, ATTEMPT takes a two-stage approach that finds a parent node and labels child nodes. This method utilizes pre-trained language models for hypernym/hyponymy recognition, leveraging local nodes with prompts to generate natural sentences. The proposed approach outperforms existing methods on both taxonomy completion and extension tasks, demonstrating its effectiveness in six domains across two public datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Taxonomies are important for organizing knowledge systems and making them useful for various applications. Right now, taxonomies can get outdated as new information becomes available. Researchers have been trying to find ways to update taxonomies, but so far, they’ve mostly focused on adding new concepts to the ends of the hierarchy. This paper presents a new way called ATTEMPT that does this differently. Instead of just adding new things at the end, ATTEMPT finds where those new concepts fit best and labels them correctly. It uses special language models to figure out how new ideas relate to each other. Tests show that ATTEMPT works better than current methods for updating taxonomies. |
