Hidden understanding models for statistical sentence understanding

被引：0
|
作者：
Schwartz, R
Miller, S
Stallard, D
Makhoul, J
机构：
来源：
1997 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I - V: VOL I: PLENARY, EXPERT SUMMARIES, SPECIAL, AUDIO, UNDERWATER ACOUSTICS, VLSI; VOL II: SPEECH PROCESSING; VOL III: SPEECH PROCESSING, DIGITAL SIGNAL PROCESSING; VOL IV: MULTIDIMENSIONAL SIGNAL PROCESSING, NEURAL NETWORKS - VOL V: STATISTICAL SIGNAL AND ARRAY PROCESSING, APPLICATIONS | 1997年
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D O I：
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中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
We describe the first sentence understanding system that is completely based on learned methods both for understanding individual sentences, and determining their meaning in the context of preceding sentences. We divide the problem into three stages: semantic parsing, semantic classification, and discourse modeling. Each of these stages requires a different model. When we ran this system on the last test (December, 1994) of the ARPA Air Travel Information System (ATIS) task, we achieved 13.7% error rate. The error rate for those sentences that are context-independent (class A) was 9.7%.
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页码：1479 / 1482
页数：4
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