Classification in NLP
- (Art Institute Chicago, Chicago, Illinois - Alvin Wei-Cheng Wong)
- Overview
In Natural Language Processing (NLP), statistical classification is a machine learning (ML) technique where text data is categorized into different classes based on statistical patterns identified from a training dataset, allowing the system to predict the category of new, unseen text by analyzing its features and comparing them to the learned patterns; essentially, it's a method of assigning labels to text based on probability calculations derived from the data.
Please refer to the following for more information:
- Wikipedia: Statistical Classification.
- Key Characteristics
Key characteristics about statistical classification in NLP:
- Supervised learning: This approach requires labeled training data where the correct category for each text sample is already known, which the model learns from to make predictions on new data.
- Feature extraction: To classify text, the system extracts relevant features like word frequencies, n-grams, or parts of speech tags, which are then used to build the classification model.
- Probability-based models: Statistical classification often relies on probabilistic algorithms like Naive Bayes, where the system calculates the probability of a text belonging to a specific category based on its features.
- Applications
Applications of statistical classification in NLP:
- Sentiment analysis: Identifying the sentiment (positive, negative, neutral) of a piece of text
- Topic classification: Categorizing documents based on their main topic
- Spam filtering: Identifying emails as spam or not spam
- Named entity recognition: Identifying named entities like people, locations, and organizations in text
