| 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,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,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,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. | |
| 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,37-1-A92-1027,ak | </term> are unknown and much of the <term> | text | </term> is irrelevant to the task . The <term> | #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. | |
| 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,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,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. |
