Concordance

Query P82-1035 31 >
Multilevel Sort 31 (1,445.3 per million)

other,37-2-P82-1035,bq	special problems for readers , such as <term>	misspelled words	</term> , <term> missing words </term> , <term>	#13011 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 asmisspelled words, missing words, poor syntactic construction, missing periods, etc.
other,39-4-P82-1035,bq	<term> elllpsis </term> ) , and resolve <term>	referents	</term> ( <term> anaphora </term> ) . This method	#13094 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 resolvereferents (anaphora).
other,4-5-P82-1035,bq	anaphora </term> ) . This method of using <term>	expectations	</term> to aid the understanding of <term>	#13103 This method of usingexpectations 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.
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,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,43-2-P82-1035,bq	</term> , <term> missing words </term> , <term>	poor syntactic construction	</term> , <term> missing periods </term> , etc	#13017 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,47-2-P82-1035,bq	poor syntactic construction </term> , <term>	missing periods	</term> , etc . Our solution to these problems	#13021 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,6-2-P82-1035,bq	</term> . However , a great deal of <term>	natural language texts	</term> e.g. , <term> memos </term> , rough <term>	#12980 However, a great deal ofnatural 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.
tech,2-1-P82-1035,bq	executed to yield the answer . Most large <term>	text-understanding systems	</term> have been designed under the assumption	#12946 Most largetext-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.
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.


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