Extracting Social Networks and Biographical Facts From Conversational Speech Transcripts
Extracting Social Networks and Biographical Facts From Conversational Speech Transcripts
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从对话语音记录中提取社交网络和传记事实
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
S. Roukos
中科院分区：
文献类型：
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作者：
Hongyan Jing;N. Kambhatla;S. Roukos
我们提出了一个通用的框架，自动提取社交网络和传记的事实，从会话讲话。我们的方法依赖于融合多个信息提取模块产生的输出，包括实体识别和检测，关系检测和事件检测模块。我们描述的具体功能和算法的改进有效的会话语音。这些累积将开发集的社交网络提取性能从0.06提高到0.30，测试集的社交网络提取性能从0.06提高到0.28，通过网络内关系的f测量来测量。同样的框架可以应用于其他类型的文本-我们已经建立了一个自动传记生成系统，一般领域的文本使用相同的方法。
We present a general framework for automatically extracting social networks and biographical facts from conversational speech. Our approach relies on fusing the output produced by multiple information extraction modules, including entity recognition and detection, relation detection, and event detection modules. We describe the specific features and algorithmic refinements effective for conversational speech. These cumulatively increase the performance of social network extraction from 0.06 to 0.30 for the development set, and from 0.06 to 0.28 for the test set, as measured by f-measure on the ties within a network. The same framework can be applied to other genres of text — we have built an automatic biography generation system for general domain text using the same approach.