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