Because chains of semantically related words express semantic continuity, such lexical chains can play an important role in the detection of malapropisms. A malapropism is a correctly spelled word that does not fit in the context where it is used because it is the result of a spelling error on a different word that was intended.
I first assume that such a word has much less probability of being inserted in any chain with other words. If this assumption is correct, words that failed to be inserted with other words can be considered as potential malapropisms. A mechanism that generates spelling replacements can then be used to generate replacement candidates. The second assumption is that whenever a spelling replacement can be inserted in a chain with other words, this replacement is likely to be the intended word for which a malapropism has been substituted.
The algorithm proposed here to detect lexical chains uses the on-line thesaurus WordNet to automatically quantify semantic relations between words. Chains identified by the algorithm may have two major problems: over- or under-chaining. Under-chaining---the inability to link a pair of related words---might be caused by an inadequacy of WordNet's set of relations, a lack of connections in WordNet's set of relations, a lack of connections in WordNet, a lack of consistency in the semantic proximity expressed by WordNet's links, and a poor algorithm for chaining. Over-chaining---the linking of two poorly related words---might happen whenever two semantically distant words are close to each other in WordNet's graph. Over-chaining often results in the merging of two chains.
The results of the experiment show the validity of the basic assumptions. However, improvements to the lexical-chaining algorithm are required before the malapropism detection algorithm can be integrated into a commercial spelling checker.
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