| 摘要 |
A system is provided for spelling correction in which the context of a word in a sentence is utilized to determine which of several alternative or possible words was intended. The probability that a particular alternative was the word that was intended is determined through Bayesian analysis utilizing multiple kinds of features of the context of the target word, such as the presence of certain characteristic words within some distance of the target word, or the presence of certain characteristic patterns of words and part-of-speech tags around the target word. The system successfully combines multiple types of features via Bayesian analysis through means for resolving egregious interdependencies among features. The system first recognizes the interdependencies, and then resolves them by deleting all but the strongest feature involved in each interdependency, thereby allowing it to make its decisions based on the strongest non-conflicting set of features. In addition, the robustness of the system's decisions is enhanced by the pruning or deletion from consideration of certain features, in one case by deleting features for which there is insufficient evidence in the training corpus to support reliable decision-making, and secondly by deleting features which are uninformative at discriminating among the alternative spellings of the target word under consideration.
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