| 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. | |
| 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,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,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. | |
| 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,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,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,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. | |
| 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,27-5-A92-1027,ak | </term> that permit certain kinds of <term> | phrases | </term> to be deduced despite the presence | #22663 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 ofphrases 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,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,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,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. | |
| 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. | |
| 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,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,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. | |
| 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,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. |
