Two-Level, Many-Paths Generation
Two-Level, Many-Paths Generation
复制标题
两级多路径生成
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
V. Hatzivassiloglou
中科院分区：
文献类型：
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作者：
Kevin Knight;V. Hatzivassiloglou
大规模的自然语言生成需要整合大量的知识：词汇，语法和概念。一个强大的生成器必须能够在知识缺失的情况下也能很好地运行。它还必须对不完整或不准确的输入具有鲁棒性。为了解决这些问题，我们构建了一个混合生成器，其中符号知识的空白通过统计方法来填补。我们描述的算法，并显示实验结果。我们还讨论了如何混合发电模型可以用来简化当前的发电机，并提高其可移植性，即使完美的知识是在原则上获得。
Large-scale natural language generation requires the integration of vast amounts of knowledge: lexical, grammatical, and conceptual. A robust generator must be able to operate well even when pieces of knowledge are missing. It must also be robust against incomplete or inaccurate inputs. To attack these problems, we have built a hybrid generator, in which gaps in symbolic knowledge are filled by statistical methods. We describe algorithms and show experimental results. We also discuss how the hybrid generation model can be used to simplify current generators and enhance their portability, even when perfect knowledge is in principle obtainable.