Concordance

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other,14-2-P82-1035,bq	</term> e.g. , <term> memos </term> , rough <term>	drafts	</term> , <term> conversation transcripts </term>	#12988 However, a great deal of natural language texts e.g., memos, roughdrafts, 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,10-5-P82-1035,bq	</term> to aid the understanding of <term>	scruffy texts	</term> has been incorporated into a working	#13109 This method of using expectations to aid the understanding ofscruffy texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages.
other,34-4-P82-1035,bq	fill in <term> missing words </term> ( <term>	elllpsis	</term> ) , and resolve <term> referents </term>	#13089 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,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,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,11-4-P82-1035,bq	expectations </term> can be used to figure out <term>	unknown words	</term> from <term> context </term> , constrain	#13066 These syntactic and semantic expectations can be used to figure outunknown words from context, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora).
other,27-1-P82-1035,bq	<term> newspaper stories </term> and other <term>	edited texts	</term> . However , a great deal of <term>	#12971 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 otheredited texts.
other,11-2-P82-1035,bq	natural language texts </term> e.g. , <term>	memos	</term> , rough <term> drafts </term> , <term>	#12985 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,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.


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