Homonym Context Pattern Generation for Word Sense Disambiguation

The system performs self-service by automatically generating its own training data through context pattern extraction from unlabeled text. The method extracts context patterns around homonym occurrences, identifies non-homonym words with similar patterns, and uses their lexical tags to create training examples without human intervention, eliminating the need for manual corpus annotation while maintaining WSD accuracy

The system performs preliminary action by pre-processing text to extract context patterns and identify candidate words before the actual WSD task. By提前 (in advance) creating structured context patterns and matching them with known lexical tags from non-homonym words, the system prepares training data automatically, saving the time that would otherwise be spent on manual annotation

The system generates its own training corpora by automatically extracting context patterns from text and matching them with lexical tags from non-homonym words. This self-service approach creates diverse training examples across multiple contexts without requiring expensive manual annotation by linguists, making corpus generation cost-effective while maintaining adaptability to various contexts

The system creates training examples by copying the context pattern structure from non-homonym words and applying it to homonym words. By replicating the contextual framework and filling it with appropriate lexical tags through pattern matching, the system generates diverse training corpora at low cost, achieving broad context coverage without prohibitive expenses