Summary of Methods For Generating Drift in Text Streams, by Cristiano Mesquita Garcia et al.
Methods for Generating Drift in Text Streams
by Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto Jr, Jean Paul Barddal
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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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 proposes four textual drift generation methods to produce datasets with labeled concept drifts for evaluating machine learning models’ ability to adapt to changing data distributions. The methods are applied to Yelp and Airbnb datasets, which are tested using incremental classifiers that respect the stream mining paradigm. The results show that all methods degrade in performance immediately after the drifts occur, but the incremental SVM recovers its previous performance levels quickly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps machines learn from text data over time by creating datasets with labeled changes in meaning. This is important because people’s opinions and words’ meanings can change over time, and we need to adapt to these changes. The researchers developed four ways to create these datasets and tested them on Yelp and Airbnb reviews. They found that all methods were affected when the meanings changed, but one method worked well at recovering from these changes. |
Keywords
* Artificial intelligence * Machine learning
