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other,15-5-A92-1027,bq	heuristics </term> based on the placement of <term>	function words	</term> , and by <term> heuristic rules </term>	#17680 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,bq	ambiguity </term> and thus the number of <term>	edges	</term> , since only <term> edges </term> with	#17737 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,bq	new <term> edge </term> is added to the <term>	chart	</term> , the algorithm checks only the topmost	#17608 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,bq	<term> syntactic categories </term> on the <term>	terminal and non-terminal edges	</term> , thereby reducing the amount of <term>	#17721 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,bq	be deduced despite the presence of <term>	unknown words	</term> . A further <term> reduction in the	#17700 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,bq	through a <term> reduction </term> of its <term>	search space	</term> . As each new <term> edge </term> is	#17597 The parser gains algorithmic efficiency through a reduction of itssearch space.
other,30-4-A92-1027,bq	</term> covers the longest possible <term>	span	</term> . This is facilitated through the	#17663 The resulting spanning 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 possiblespan.
other,18-3-A92-1027,bq	algorithm checks only the topmost of the <term>	edges	</term> adjacent to it , rather than all	#17618 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,bq	</term> , thereby reducing the amount of <term>	ambiguity	</term> and thus the number of <term> edges	#17731 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,2-4-A92-1027,bq	conventional treatments . The resulting <term>	spanning edges	</term> are insured to be the correct ones	#17635 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,bq	carefully controlling the order in which <term>	edges	</term> are introduced so that every final	#17651 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,bq	unrestricted texts </term> where many of the <term>	words	</term> are unknown and much of the <term>	#17573 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,bq	adjacent to it , rather than all such <term>	edges	</term> as in conventional treatments . The	#17627 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,7-5-A92-1027,bq	is facilitated through the use of <term>	phrase boundary heuristics	</term> based on the placement of <term> function	#17672 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,25-4-A92-1027,bq	are introduced so that every final <term>	constituent	</term> covers the longest possible <term>	#17658 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,1-2-A92-1027,bq	</term> is irrelevant to the task . The <term>	parser	</term> gains algorithmic efficiency through	#17588 Theparser gains algorithmic efficiency through a reduction of its search space.
other,42-6-A92-1027,bq	only <term> edges </term> with a valid <term>	semantic	</term> interpretation are ever introduced	#17745 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,2-6-A92-1027,bq	<term> unknown words </term> . A further <term>	reduction in the search space	</term> is achieved by using <term> semantic	#17705 A furtherreduction 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 valid semantic interpretation are ever introduced.
other,3-3-A92-1027,bq	<term> search space </term> . As each new <term>	edge	</term> is added to the <term> chart </term>	#17603 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,37-1-A92-1027,bq	</term> are unknown and much of the <term>	text	</term> is irrelevant to the task . The <term>	#17580 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.


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