Concordance

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other,40-2-P82-1035,bq	such as <term> misspelled words </term> , <term>	missing words	</term> , <term> poor syntactic construction	#13014 However, a great deal of natural language texts 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.
other,21-3-P82-1035,bq	<term> surface English </term> and on <term>	world knowledge	</term> of the situation being described	#13047 Our solution to these problems is to make use of expectations, based both on knowledge of surface English and onworld knowledge of the situation being described.
other,41-4-P82-1035,bq	and resolve <term> referents </term> ( <term>	anaphora	</term> ) . This method of using <term> expectations	#13096 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 (elllpsis), and resolve referents (anaphora).
other,14-4-P82-1035,bq	out <term> unknown words </term> from <term>	context	</term> , constrain the possible <term> word-senses	#13069 These syntactic and semantic expectations can be used to figure out unknown words fromcontext, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora).
other,13-1-P82-1035,bq	under the assumption that the input <term>	text	</term> will be in reasonably neat form ,	#12957 Most large text-understanding systems have been designed under the assumption that the inputtext will be in reasonably neat form, e.g., newspaper stories and other edited texts.
other,21-4-P82-1035,bq	possible <term> word-senses </term> of <term>	words with multiple meanings	</term> ( <term> ambiguity </term> ) , fill in	#13076 These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses ofwords with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora).
other,1-4-P82-1035,bq	situation being described . These <term>	syntactic and semantic expectations	</term> can be used to figure out <term> unknown	#13056 Thesesyntactic 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 (elllpsis), and resolve referents (anaphora).
other,26-4-P82-1035,bq	words with multiple meanings </term> ( <term>	ambiguity	</term> ) , fill in <term> missing words </term>	#13081 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 (elllpsis), and resolve referents (anaphora).
tool,21-5-P82-1035,bq	<term> computer program </term> called <term>	NOMAD	</term> , which understands <term> scruffy	#13120 This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program calledNOMAD, which understands scruffy texts in the domain of Navy messages.
other,16-2-P82-1035,bq	</term> , rough <term> drafts </term> , <term>	conversation transcripts	</term> etc. , have features that differ	#12990 However, a great deal of natural language texts 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.
tech,18-5-P82-1035,bq	has been incorporated into a working <term>	computer program	</term> called <term> NOMAD </term> , which understands	#13117 This method of using expectations to aid the understanding of scruffy texts has been incorporated into a workingcomputer program called NOMAD, which understands scruffy texts in the domain of Navy messages.


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