The measurement of textual coherence with latent semantic analysis
The measurement of textual coherence with latent semantic analysis
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DOI：
10.1080/01638539809545029
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发表时间：
1998-01-01
影响因子：
2.2
通讯作者：
Landauer, TK
中科院分区：
文献类型：
--
作者：
Foltz, PW;Kintsch, W;Landauer, TK
潜在语义分析（LSA）是一种用来衡量语篇连贯性的技术。通过比较高维语义空间中的文本的2个相邻段的向量，该方法提供了段之间的语义相关度的表征。我们通过重新分析两项研究中的文本集来说明预测连贯性的方法，这两项研究操纵了文本的连贯性并评估了读者的理解。结果表明，该方法能够预测语篇连贯性对理解的影响，并且比简单的词-词重叠测量更有效。通过这种方式，LSA可以作为一种自动化方法，产生类似于命题建模的一致性预测。我们描述了额外的研究调查应用LSA分析语篇结构，并检查潜在的LSA作为一个心理模型的连贯性在文本理解的影响。
Latent Semantic Analysis (LSA) is used as a technique for measuring the coherence of texts. By comparing the vectors for 2 adjoining segments of text in a high-dimensional semantic space, the method provides a characterization of the degree of semantic relatedness between the segments. We illustrate the approach for predicting coherence through reanalyzing sets of texts from 2 studies that manipulated the coherence of texts and assessed readers' comprehension. The results indicate that the method is able to predict the effect of text coherence on comprehension and is more effective than simple term-term overlap measures. In this manner, LSA can be applied as an automated method that produces coherence predictions similar to propositional modeling. We describe additional studies investigating the application of LSA to analyzing discourse structure and examine the potential of LSA as a psychological model of coherence effects in text comprehension.