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Preview of Massive-Scale Named Entity Disambiguation with Deep Learning and Data Augmentation
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📄 Pages: 10 pages
⬇️ Downloads: 65

Summary

Traditional NED algorithms use a single model for all entities, but this method handles the complexity of disambiguating a large number of ambiguous mentions.

Key Points:

1. Data Distribution and Solution: Due to the long tail distribution of training instances (most mentions have limited data), the authors employ data augmentation techniques and transfer learning to overcome sparse data issues.

2. Model Architecture: They propose deep learning models, specifically Word Expert models, where a separate classifier is trained for each target mention string. This allows for more focused learning on specific mentions and their associated entities.

3. Text Representation Alternatives: The paper explores various text representation options, including bag-of-word embeddings and LSTMs, to benchmark their performance on the massive dataset. They find that bag-of-word embeddings perform better with scarce training data, while LSTMs excel with larger datasets.

4. Transfer Learning: Transferring an LSTM model learned from a large out-of-domain dataset (Wikipedia) proves most effective for Word Expert models across different frequency bands of mention occurrences.

5. Experiments and Results: The system outperforms comparable NED systems trained on in-domain data, demonstrating the effectiveness of the proposed approach and transfer learning.

6. Contribution: This work provides a valuable experimental framework for testing text representation and classification algorithms, contributing to advancements in natural language understanding through end-to-end learning of representations and classifiers.

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Description

It proposes data augmentation and transfer learning to address scarce training data, demonstrating that bag-of-word embeddings outperform LSTMs in low-data scenarios, while LSTMs excel with larger datasets.

Technical Information

  • File Format: PDF
  • File Size: 360 KB
  • Pages: 10
  • Language: EN
  • Total Downloads: 65
  • Last Updated: 2 hours ago

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