Statistical NLP
- [McGill University]
- Overview
Statistical NLP uses probabilistic and statistical methods to enable machines to process human language by analyzing patterns in large amounts of text.
Instead of relying on hand-crafted rules, it uses machine learning algorithms to learn from data, leading to more robust systems that can handle errors and new language.
Key applications include predicting the next word in a sequence (language modeling) and performing tasks like part-of-speech tagging and text categorization.
1. Core principles:
- Machine learning from data: Statistical NLP uses algorithms to learn linguistic patterns from large collections of text (corpora), such as the probability of a word sequence or the likelihood of a specific grammatical structure.
- Probabilistic models: It relies on probability theory and statistical inference to represent and process language. This allows models to handle uncertainty and make predictions based on evidence from the data.
- Contrast with rule-based systems: It is a data-driven approach that contrasts with older rule-based systems, which required a linguist to write down every possible grammatical rule. Statistical models are generally more robust to errors and unfamiliar language because they learn from exceptions and common cases found in the data.
2. Key applications and techniques:
- Language modeling: Predicting the probability of a word sequence, which is fundamental for tasks like speech recognition and text generation.
- Part-of-speech tagging: Automatically labeling words with their grammatical category (e.g., noun, verb, adjective).
- Text categorization: Assigning a document to a predefined category, such as spam filtering or topic classification.
- Probabilistic parsing: Inferring the grammatical structure of a sentence.
- Word sense disambiguation: Determining the correct meaning of a word when it has multiple possible definitions.
3. Evolution to deep learning:
While deep learning models have become dominant in many areas, they can be viewed as a modern evolution of statistical NLP because they are also fundamentally statistical and data-driven.
Neural machine translation, for example, directly learns sequence-to-sequence transformations, often bypassing the intermediate statistical models (like word alignment and language modeling) that were used in older statistical machine translation systems.
[More to come ...]
