Supervised disambiguation Training set: exemplars where each occurrence of the ambiguous word w is annotated with a semantic label This becomes a statistical classification problem; assign w some sense sk in context cl Approaches Bavesian Classification: the context of occurrence is treated as a bag of words without structure, but it integrates Information from many words in a context window. nformation Theory: only looks at the most informative feature in the context, which may be sensitive to text structure. T here are many more approaches (see Chapter 16 or a text on Machine Learning ml) that could be applied 20212/5 Natural Language Processing--Word Sense Disambiguation 62021/2/5 Natural Language Processing -- Word Sense Disambiguation 6 Supervised Disambiguation ◼ Training set: exemplars where each occurrence of the ambiguous word w is annotated with a semantic label. This becomes a statistical classification problem; assign w some sense sk in context cl. ◼ Approaches: ◼ Bayesian Classification: the context of occurrence is treated as a bag of words without structure, but it integrates information from many words in a context window. ◼ Information Theory: only looks at the most informative feature in the context, which may be sensitive to text structure. ◼ There are many more approaches (see Chapter 16 or a text on Machine Learning (ML)) that could be applied