| 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,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. | |
| 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,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,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. | |
| 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,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,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,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,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. |
