Concordance

tech,4-1-A92-1027,ak

other,28-6-A92-1027,ak

other,3-2-A92-1027,ak

tech,6-1-A92-1027,ak

other,2-4-A92-1027,ak

other,38-6-A92-1027,ak

other,10-2-A92-1027,ak

other,42-6-A92-1027,ak

other,30-1-A92-1027,ak

other,18-6-A92-1027,ak

other,14-6-A92-1027,ak	achieved by using semantic rather than <term>	syntactic categories	</term> on the <term> terminal and non-terminal	#22688 A further reduction in the search space is achieved by using semantic rather thansyntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced.
tech,4-1-A92-1027,ak	presented . We present an efficient <term>	algorithm	</term> for <term> chart-based phrase structure	#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.
other,24-1-A92-1027,ak	specific <term> information </term> from <term>	unrestricted texts	</term> where many of the <term> words </term>	#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,28-6-A92-1027,ak	</term> , thereby reducing the amount of <term>	ambiguity	</term> and thus the number of <term> edges	#22702 A further reduction in the search space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount ofambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced.
other,5-6-A92-1027,ak	</term> . A further reduction in the <term>	search space	</term> is achieved by using semantic rather	#22679 A further reduction in thesearch space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced.
other,3-2-A92-1027,ak	task . The <term> parser </term> gains <term>	algorithmic efficiency	</term> through a reduction of its <term> search	#22561 The parser gainsalgorithmic efficiency through a reduction of its search space.
other,3-3-A92-1027,ak	<term> search space </term> . As each new <term>	edge	</term> is added to the <term> chart </term>	#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.
tech,6-1-A92-1027,ak	efficient <term> algorithm </term> for <term>	chart-based phrase structure parsing	</term> of <term> natural language </term> that	#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.
tech,1-2-A92-1027,ak	</term> is irrelevant to the task . The <term>	parser	</term> gains <term> algorithmic efficiency	#22559 Theparser gains algorithmic efficiency through a reduction of its search space.
other,2-4-A92-1027,ak	conventional treatments . The resulting <term>	spanning edges	</term> are insured to be the correct ones	#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,11-1-A92-1027,ak	phrase structure parsing </term> of <term>	natural language	</term> that is tailored to the problem of	#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,38-6-A92-1027,ak	number of <term> edges </term> , since only <term>	edges	</term> with a valid <term> semantic interpretation	#22712 A further reduction in the search space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since onlyedges with a valid semantic interpretation are ever introduced.
other,34-6-A92-1027,ak	ambiguity </term> and thus the number of <term>	edges	</term> , since only <term> edges </term> with	#22708 A further reduction in the search space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number ofedges, since only edges with a valid semantic interpretation are ever introduced.
other,10-2-A92-1027,ak	efficiency </term> through a reduction of its <term>	search space	</term> . As each new <term> edge </term> is	#22568 The parser gains algorithmic efficiency through a reduction of itssearch space.
model,20-5-A92-1027,ak	<term> function words </term> , and by <term>	heuristic rules	</term> that permit certain kinds of <term>	#22656 This is facilitated through the use of phrase boundary heuristics based on the placement of function words, and byheuristic rules that permit certain kinds of phrases to be deduced despite the presence of unknown words.
other,42-6-A92-1027,ak	only <term> edges </term> with a valid <term>	semantic interpretation	</term> are ever introduced . In this paper	#22716 A further reduction in the search space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a validsemantic interpretation are ever introduced.
other,8-3-A92-1027,ak	new <term> edge </term> is added to the <term>	chart	</term> , the <term> algorithm </term> checks	#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.
other,30-1-A92-1027,ak	unrestricted texts </term> where many of the <term>	words	</term> are unknown and much of the <term>	#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,27-3-A92-1027,ak	adjacent to it , rather than all such <term>	edges	</term> as in conventional treatments . The	#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,18-6-A92-1027,ak	<term> syntactic categories </term> on the <term>	terminal and non-terminal edges	</term> , thereby reducing the amount of <term>	#22692 A further reduction in the search space is achieved by using semantic rather than syntactic categories on theterminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced.


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