Summary of Queryner: Segmentation Of E-commerce Queries, by Chester Palen-michel et al.
QueryNER: Segmentation of E-commerce Queries
by Chester Palen-Michel, Lizzie Liang, Zhe Wu, Constantine Lignos
First submitted to arxiv on: 15 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 QueryNER is a novel approach to e-commerce query segmentation, departing from traditional aspect-value extraction methods that focus on specific product features. Instead, our model and accompanying dataset aim to divide queries into meaningful, broadly applicable chunks. We present baseline tagging results and compare token and entity dropping techniques for null and low recall query recovery. Our experiments demonstrate the effectiveness of simple data augmentation strategies in enhancing model robustness against noise. The QueryNER dataset is publicly available, providing a valuable resource for researchers working on e-commerce-related natural language processing tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper presents a new way to break down search queries into smaller parts that are meaningful and useful. Unlike previous work, which focused on finding specific details about products, this approach aims to divide queries into broader categories. The team developed a model and dataset called QueryNER and tested it by comparing different methods for dealing with missing or incomplete data. They found that using simple techniques to make the model more robust can improve its performance. This dataset is now available to others who want to work on similar tasks. |
Keywords
» Artificial intelligence » Data augmentation » Natural language processing » Recall » Token
