Scruffy text understanding

Most large text-understanding systems have been designed under the assumption that the input text will be in reasonably "neat" form, e.g., newspaper stories and other edited texts. However, a great deal of natural language text, e.g., memos, rough drafts, conversation transcripts, etc., have features that differ significantly from "neat" texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc. Our solution to these problems is to make use of expectations, based both on knowledge of surface English and on world knowledge of the situation being described. These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (ellipsis), and resolve referents (anaphora). This method of using expectations to aid the understanding of "scruffy" texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages.