Concordance

Query A92-1027 30 >
Random sample 30 (960.5 per million)

tech,4-1-A92-1027,ak	We present an efficient <term> algorithm </term> for <term> chart-based phrase structure parsing </term> of <term> natural language </term> that is tailored to the problem of extracting specific <term> information </term> from <term> unrestricted texts </term> where many of the <term> words </term> are unknown and much of the <term> text </term> is irrelevant to the task .	#22518 We present an efficientalgorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of the text is irrelevant to the task.
tech,6-1-A92-1027,ak	We present an efficient <term> algorithm </term> for <term> chart-based phrase structure parsing </term> of <term> natural language </term> that is tailored to the problem of extracting specific <term> information </term> from <term> unrestricted texts </term> where many of the <term> words </term> are unknown and much of the <term> text </term> is irrelevant to the task .	#22520 We present an efficient algorithm forchart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of the text is irrelevant to the task.
other,11-1-A92-1027,ak	We present an efficient <term> algorithm </term> for <term> chart-based phrase structure parsing </term> of <term> natural language </term> that is tailored to the problem of extracting specific <term> information </term> from <term> unrestricted texts </term> where many of the <term> words </term> are unknown and much of the <term> text </term> is irrelevant to the task .	#22525 We present an efficient algorithm for chart-based phrase structure parsing ofnatural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of the text is irrelevant to the task.
other,22-1-A92-1027,ak	We present an efficient <term> algorithm </term> for <term> chart-based phrase structure parsing </term> of <term> natural language </term> that is tailored to the problem of extracting specific <term> information </term> from <term> unrestricted texts </term> where many of the <term> words </term> are unknown and much of the <term> text </term> is irrelevant to the task .	#22536 We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specificinformation from unrestricted texts where many of the words are unknown and much of the text is irrelevant to the task.
other,24-1-A92-1027,ak	We present an efficient <term> algorithm </term> for <term> chart-based phrase structure parsing </term> of <term> natural language </term> that is tailored to the problem of extracting specific <term> information </term> from <term> unrestricted texts </term> where many of the <term> words </term> are unknown and much of the <term> text </term> is irrelevant to the task .	#22538 We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information fromunrestricted texts where many of the words are unknown and much of the text is irrelevant to the task.
other,30-1-A92-1027,ak	We present an efficient <term> algorithm </term> for <term> chart-based phrase structure parsing </term> of <term> natural language </term> that is tailored to the problem of extracting specific <term> information </term> from <term> unrestricted texts </term> where many of the <term> words </term> are unknown and much of the <term> text </term> is irrelevant to the task .	#22544 We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of thewords are unknown and much of the text is irrelevant to the task.
other,37-1-A92-1027,ak	We present an efficient <term> algorithm </term> for <term> chart-based phrase structure parsing </term> of <term> natural language </term> that is tailored to the problem of extracting specific <term> information </term> from <term> unrestricted texts </term> where many of the <term> words </term> are unknown and much of the <term> text </term> is irrelevant to the task .	#22551 We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of thetext is irrelevant to the task.
tech,1-2-A92-1027,ak	The <term> parser </term> gains <term> algorithmic efficiency </term> through a reduction of its <term> search space </term> .	#22559 Theparser gains algorithmic efficiency through a reduction of its search space.
other,3-2-A92-1027,ak	The <term> parser </term> gains <term> algorithmic efficiency </term> through a reduction of its <term> search space </term> .	#22561 The parser gainsalgorithmic efficiency through a reduction of its search space.
other,10-2-A92-1027,ak	The <term> parser </term> gains <term> algorithmic efficiency </term> through a reduction of its <term> search space </term> .	#22568 The parser gains algorithmic efficiency through a reduction of itssearch space.
other,3-3-A92-1027,ak	As each new <term> edge </term> is added to the <term> chart </term> , the <term> algorithm </term> checks only the topmost of the <term> edges </term> adjacent to it , rather than all such <term> edges </term> as in conventional treatments .	#22574 As each newedge is added to the chart, the algorithm checks only the topmost of the edges adjacent to it, rather than all such edges as in conventional treatments.
other,8-3-A92-1027,ak	As each new <term> edge </term> is added to the <term> chart </term> , the <term> algorithm </term> checks only the topmost of the <term> edges </term> adjacent to it , rather than all such <term> edges </term> as in conventional treatments .	#22579 As each new edge is added to thechart, the algorithm checks only the topmost of the edges adjacent to it, rather than all such edges as in conventional treatments.
tech,11-3-A92-1027,ak	As each new <term> edge </term> is added to the <term> chart </term> , the <term> algorithm </term> checks only the topmost of the <term> edges </term> adjacent to it , rather than all such <term> edges </term> as in conventional treatments .	#22582 As each new edge is added to the chart, thealgorithm checks only the topmost of the edges adjacent to it, rather than all such edges as in conventional treatments.
other,18-3-A92-1027,ak	As each new <term> edge </term> is added to the <term> chart </term> , the <term> algorithm </term> checks only the topmost of the <term> edges </term> adjacent to it , rather than all such <term> edges </term> as in conventional treatments .	#22589 As each new edge is added to the chart, the algorithm checks only the topmost of theedges adjacent to it, rather than all such edges as in conventional treatments.
other,27-3-A92-1027,ak	As each new <term> edge </term> is added to the <term> chart </term> , the <term> algorithm </term> checks only the topmost of the <term> edges </term> adjacent to it , rather than all such <term> edges </term> as in conventional treatments .	#22598 As each new edge is added to the chart, the algorithm checks only the topmost of the edges adjacent to it, rather than all suchedges as in conventional treatments.
other,2-4-A92-1027,ak	The resulting <term> spanning edges </term> are insured to be the correct ones by carefully controlling the order in which <term> edges </term> are introduced so that every final <term> constituent </term> covers the longest possible span .	#22606 The resultingspanning edges are insured to be the correct ones by carefully controlling the order in which edges are introduced so that every final constituent covers the longest possible span.
other,18-4-A92-1027,ak	The resulting <term> spanning edges </term> are insured to be the correct ones by carefully controlling the order in which <term> edges </term> are introduced so that every final <term> constituent </term> covers the longest possible span .	#22622 The resulting spanning edges are insured to be the correct ones by carefully controlling the order in whichedges are introduced so that every final constituent covers the longest possible span.
other,25-4-A92-1027,ak	The resulting <term> spanning edges </term> are insured to be the correct ones by carefully controlling the order in which <term> edges </term> are introduced so that every final <term> constituent </term> covers the longest possible span .	#22629 The resulting spanning edges are insured to be the correct ones by carefully controlling the order in which edges are introduced so that every finalconstituent covers the longest possible span.
model,7-5-A92-1027,ak	This is facilitated through the use of <term> phrase boundary heuristics </term> based on the placement of <term> function words </term> , and by <term> heuristic rules </term> that permit certain kinds of <term> phrases </term> to be deduced despite the presence of <term> unknown words </term> .	#22643 This is facilitated through the use ofphrase boundary heuristics based on the placement of function words, and by heuristic rules that permit certain kinds of phrases to be deduced despite the presence of unknown words.
other,15-5-A92-1027,ak	This is facilitated through the use of <term> phrase boundary heuristics </term> based on the placement of <term> function words </term> , and by <term> heuristic rules </term> that permit certain kinds of <term> phrases </term> to be deduced despite the presence of <term> unknown words </term> .	#22651 This is facilitated through the use of phrase boundary heuristics based on the placement offunction words, and by heuristic rules that permit certain kinds of phrases to be deduced despite the presence of unknown words.


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