Memory-Based Text Chunking
Memory-Based Text Chunking
复制标题
基于记忆的文本分块
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
Tilburg University
中科院分区：
文献类型：
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作者：
J. Veenstra;Tilburg University
数据量的巨大往往会影响信息检索瓦尔、信息抽取和分析的计算效率。已经提出了几种数据简化技术来解决这个问题。计算语言学组块中数据约简的一种新方法。在（Daelemans，布赫霍尔茨，and Veenstra，1999）中，我们描述了在主语/宾语识别的背景下，使用基于记忆的学习（MBL）进行的NP和VP（名词和动词短语）组块的实验。在本文中，我们将表明，NP，VP和PP（介词短语）组块是可能的，准确率和召回率约为94- 95%。
The abundance of data often hinders computational efficiency in information retrie val, information extraction and parsing. Several data reduction techniques have been proposed t o v rcome this problem. A recently blooming approach to data reduction in computational linguist ics chunking. In (Daelemans, Buchholz, and Veenstra, 1999), we describe experiments on NP and V P (noun and verb phrase) chunking using Memory-Based Learning ( MBL ) carried out in the context of subject/object identification. In this paper we will show that NP, VP and PP ( prepositional phrase) chunking is possible with a precision and recall around 94-95%.