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Questions and Answers
What refers to a word, phrase, or token that is not present in the training data or vocabulary of a model in NLP?
What type of unknown refers to words that are not seen during training but may be seen during testing or deployment?
What is a challenge that models may face when encountering unknowns?
What technique involves breaking down unknown words into subwords or character-level representations?
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What type of models operate on character-level representations rather than word-level representations?
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What technique involves representing unknowns with a special 'UNK' token?
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Study Notes
Unknown in Natural Language Processing (NLP)
Definition of Unknown
- In NLP, an "unknown" refers to a word, phrase, or token that is not present in the training data or vocabulary of a model.
- Unknowns can be out-of-vocabulary (OOV) words, special characters, or tokens that are not recognized by the model.
Types of Unknowns
- Out-of-vocabulary (OOV) words: Words that are not present in the training data or vocabulary of a model.
- Unseen words: Words that are not seen during training but may be seen during testing or deployment.
- Special tokens: Tokens that are not part of the standard language, such as emojis, hashtags, or URLs.
Challenges of Unknowns
- Vocabulary mismatch: Models may not be able to handle unknowns, leading to errors or misclassifications.
- Overfitting: Models may overfit to the training data, failing to generalize to unknowns.
- Lack of robustness: Models may be brittle and fail to perform well when encountering unknowns.
Techniques for Handling Unknowns
- Subwording: Breaking down unknown words into subwords or character-level representations to improve model performance.
- Character-level models: Models that operate on character-level representations, rather than word-level representations.
- UNK token: Representing unknowns with a special "UNK" token, allowing the model to learn a representation for unknowns.
- Vocabulary expansion: Expanding the vocabulary of a model to include more words, reducing the likelihood of unknowns.
Importance of Handling Unknowns
- Robustness: Handling unknowns improves the robustness of NLP models, enabling them to perform well in real-world scenarios.
- Generalization: Models that can handle unknowns are better able to generalize to new, unseen data.
- Real-world applications: Handling unknowns is crucial in real-world applications, such as language translation, text classification, and chatbots.
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Description
Learn about unknowns in NLP, including types of unknowns, challenges, and techniques for handling them. Improve your model's robustness and generalization capabilities.
