Summary of Benchmarking Pre-trained Text Embedding Models in Aligning Built Asset Information, by Mehrzad Shahinmoghadam et al.
Benchmarking pre-trained text embedding models in aligning built asset information
by Mehrzad Shahinmoghadam, Ali Motamedi
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper presents a comprehensive evaluation of state-of-the-art text embedding models’ ability to represent built asset technical terminology. It proposes six datasets derived from renowned data classification dictionaries for tasks such as clustering, retrieval, and reranking. The study benchmarks these models across the proposed datasets, highlighting the need for future research on domain adaptation techniques. This work aims to facilitate the automation of cross-mapping of built asset data, reducing manual reliance on domain experts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to help computers understand built asset information better. Right now, people have to do this job manually, which is time-consuming and might not be accurate. Some new AI models can help with this by organizing text into meaningful groups. The researchers tested these models using different datasets and tasks to see how well they work for built assets. They found that more research is needed to make these models better for this specific area. |
Keywords
» Artificial intelligence » Classification » Clustering » Domain adaptation » Embedding
