Concordance

other,34-6-A92-1027,ak

other,18-6-A92-1027,ak

other,10-2-A92-1027,ak

other,28-6-A92-1027,ak

other,2-4-A92-1027,ak

other,30-1-A92-1027,ak

model,7-5-A92-1027,ak

other,25-4-A92-1027,ak

other,22-1-A92-1027,ak

other,5-6-A92-1027,ak

other,15-5-A92-1027,ak	heuristics </term> based on the placement of <term>	function words	</term> , and by <term> heuristic rules </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.
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,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,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.
other,35-5-A92-1027,ak	be deduced despite the presence of <term>	unknown words	</term> . A further reduction in the <term>	#22671 This is facilitated through the use of phrase 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 ofunknown words.
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.
other,18-3-A92-1027,ak	</term> checks only the topmost of the <term>	edges	</term> adjacent to it , rather than all	#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,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,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,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,18-4-A92-1027,ak	carefully controlling the order in which <term>	edges	</term> are introduced so that every final	#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,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.
model,7-5-A92-1027,ak	is facilitated through the use of <term>	phrase boundary heuristics	</term> based on the placement of <term> function	#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.
tech,11-3-A92-1027,ak	added to the <term> chart </term> , the <term>	algorithm	</term> checks only the topmost of the <term>	#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,25-4-A92-1027,ak	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.
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,22-1-A92-1027,ak	the problem of extracting specific <term>	information	</term> from <term> unrestricted texts </term>	#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.
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,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.


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