The measurement of textual coherence with latent semantic analysis

Foltz, Peter W.; Kintsch, Walter; Landauer, Thomas K.

doi:10.1080/01638539809545029

Cited by 540 publications

(362 citation statements)

References 12 publications

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“…To obtain a rough estimate of readability for the black hole text, we used the sentence-to-sentence comparison feature on the Latent Semantic Analysis website (http://lsa.colorado.edu/). The average sentence-to-sentence cosine for a translated version of the black hole text was .39, indicating that the text was highly coherent (Foltz et al 1998). …”

Section: Methodsmentioning

confidence: 99%

The Effect of Testing on the Retention of Coherent and Incoherent Text Material

2015

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Research has shown that testing during learning can enhance the long-term retention of text material. In two experiments, we investigated the testing effect with a fill-in-the-blank test on the retention of text material. In Experiment 1, using a coherent text, we found no retention benefit of testing compared to a restudy (control) condition. In Experiment 2, text coherence was disrupted by scrambling the order of the sentences from the text. The material was subsequently presented as a list of facts as opposed to connected discourse. For the incoherent version of the text, testing slowed down the rate of forgetting compared to a restudy (control) condition. The results suggest that the connectedness of materials can play an important role in determining the magnitude of testing benefits for long-term retention. Testing with a completion test seems most beneficial for unconnected materials and less so for highly structured materials.Keywords The testing effect . Retrieval practice . Text coherence . Long-term retention In recent years, there has been a resurgence of interest for the potential benefits of testing on long-term retention. Research has shown that taking tests during learning can have profound effects on later recall compared to less demanding learning strategies like repeated study Karpicke 2006a, 2006b). The general findings are especially surprising, since repeated study will most often result in superior performance on a recall test given shortly after learning. However, this short-term benefit is not long lasting. Repeated study will generally result in a relatively fast rate of forgetting, while successful retrieval of information during learning slows down the rate of forgetting (Carpenter et al. 2008;Wheeler et al. 2003). Consequently, testing generally results in superior recall performance after a relatively long retention interval. This so-called testing effect (also known as the retrieval practice effect) has been found with different types of materials and different types of tests and using a variety of retention interval conditions (Roediger and Karpicke 2006a). Claims have been made that the testing effect is of critical importance for education, and these claims have been corroborated by studies replicating Educ Psychol Rev (2015) 27:305-315

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Section: Methodsmentioning

confidence: 99%

The Effect of Testing on the Retention of Coherent and Incoherent Text Material

2015

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“…Mitchell and Lapata in [9] follow Foltz [21] and assume that the contribution of syntactic structure can be ignored, while the meaning of a phrase is simply the commutative sum of the meanings of its constituent words. More formally, [9] defines the composition p • = u • v of vectors u and v through an additive class of composition functions expressed by:…”

Section: Distributional Compositional Semantic Modelsmentioning

confidence: 99%

Space Projections as Distributional Models for Semantic Composition

Annesi

Storch

Basili

2012

Computational Linguistics and Intelligent Text Processing

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Abstract. Empirical distributional methods account for the meaning of syntactic structures by combining words according to algebraic operators (e.g. tensor product) acting over the corresponding lexical constituents. In this paper, a novel approach for semantic composition based on space projection techniques over the basic geometric lexical representations is proposed. In line with Frege's context principle, the meaning of a phrase is modeled in terms of the subset of properties shared by the co-occurring words. In the geometric perspective here pursued, syntactic bi-grams are projected in the so called Support Subspace, aimed at emphasizing the semantic features shared by the compound words and better capturing phrase-specific aspects of the involved lexical meanings. State-of-the-art results are achieved in a well known phrase similarity task, used as a benchmark for this class of methods.

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“…[17,18,21] Most importantly, the temporal order of words in a sentence represents the differences in meaning as a result of differences in word order or syntactic structure. [18] Early semantic space modeling approaches (e.g., Foltz's [22] ) were insensitive to word ordering and thus could not capture meaning differences that are modulated by differences in syntactic structure. Consider, for example, the following two sentences from Landauer and Dumais [23] that happen to use the same vocabulary but are still semantically unrelated:…”

Section: Measure Of Semantic Relatednessmentioning

confidence: 99%

Information-based methods for evaluating the semantics of automatically generated test items

Latifi

Gierl

Ren

et al. 2016

AIR

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Multiple-choice questions are the popular type of test items that are used for testing the knowledge of health-science students in north America and elsewhere. The motivation of this article is to present the recent advances in the automatic item generation (AIG) and to propose a novel unsupervised approach that extends the information-based Compositional Distributional Semantic Model (CDSM) to measure the semantic relatedness among the pool of automatically generated items. We have used operational item bank from the medical science domain for developing the CDSM and demonstrated our approach using the concepts from AIG research. We illustrated our approach using eleven item models from the medical education domain, and discussed the possible applications to advance the AIG research.

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The measurement of textual coherence with latent semantic analysis

Cited by 540 publications

References 12 publications

The Effect of Testing on the Retention of Coherent and Incoherent Text Material

The Effect of Testing on the Retention of Coherent and Incoherent Text Material

Space Projections as Distributional Models for Semantic Composition

Information-based methods for evaluating the semantics of automatically generated test items

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