Large-scale latent semantic analysis

Latent semantic analysis (LSA) is a statistical technique for representing word meaning that has been widely used for making semantic similarity judgments between words, sentences, and documents. In order to perform an LSA analysis, an LSA space is created in a two-stage procedure, involving the construction of a word frequency matrix and the dimensionality reduction of that matrix through singular value decomposition (SVD). This article presents LANSE, an SVD algorithm specifically designed for LSA, which allows extremely large matrices to be processed using off-the-shelf computer hardware.     

Latent semantic analysis for text-based research

Latent semantic analysis (LSA) is a statistical model of word usage that permits comparisons of semantic similarity between pieces of textual information. This paper summarizes three experiments that illustrate how LSA may be used in text-based research. Two experiments describe methods for analyzing a subject’s essay for determining from what text a subject learned the information and for grading the quality of information cited in the essay. The third experiment describes using LSA to measure the coherence and comprehensibility of texts.     

Computerizing reading training: Evaluation of a latent semantic analysis space for science text

The effectiveness of a domain-specific latent semantic analysis (LSA) in assessing reading strategies was examined. Students were given self-explanation reading training (SERT) and asked to think aloud after each sentence in a science text. Novice and expert human raters and two LSA spaces (general reading, science) rated the similarity of each think-aloud protocol to benchmarks representing three different reading strategies (minimal, local, and global). The science LSA space correlated highly with human judgments, and more highly than did the general reading space. Also, cosines from the science LSA spaces can distinguish between different levels of semantic similarity, but may have trouble in distinguishing local processing protocols. Thus, a domain-specific LSA space is advantageous regardless of the size of the space. The results are discussedin the context of applying the science LSA to a computer-based version of SERT that gives online feedback based on LSA cosines.     

Using latent semantic analysis to assess reader strategies

We tested a computer-based procedure for assessing reader strategies that was based on verbal protocols that utilized latent semantic analysis (LSA). Students were given self-explanation—reading training (SERT), which teaches strategies that facilitate self-explanation during reading, such as elaboration based on world knowledge and bridging between text sentences. During a computerized version of SERT practice, students read texts and typed self-explanations into a computer after each sentence. The use of SERT strategies during this practice was assessed by determining the extent to which students used the information in the current sentence versus the prior text or world knowledge in their self-explanations. This assessment was made on the basis of human judgments and LSA. Both human judgments and LSA were remarkably similar and indicated that students who were not complying with SERT tended to paraphrase the text sentences, whereas students who were compliant with SERT tended to explain the sentences in terms of what they knew about the world and of information provided in the prior text context. The similarity between human judgments and LSA indicates that LSA will be useful in accounting for reading strategies in a Web-based version of SERT.     

Identifying reading strategies using latent semantic analysis: Comparing semantic benchmarks

We explored methods of using latent semantic analysis (LSA) to identify reading strategies in students’ self-explanations that are collected as part of a Web-based reading trainer. In this study, college students self-explained scientific texts, one sentence at a time. LSA was used to measure the similarity between the self-explanations andsemantic benchmarks (groups of words and sentences that together represent reading strategies). Three types of semantic benchmarks were compared: content words, exemplars, and strategies. Discriminant analyses were used to classify global and specific reading strategies using the LSA cosines. All benchmarks contributed to the classification of general reading strategies, but the exemplars did the best in distinguishing subtle semantic differences between reading strategies. Pragmatic and theoretical concerns of using LSA are discussed.     

Assessing short summaries with human judgments procedure and latent semantic analysis in narrative and expository texts

In the present study, we tested a computer-based procedure for assessing very concise summaries (50 words long) of two types of text (narrative and expository) using latent semantic analysis (LSA) in comparison with the judgments of four human experts. LSA was used to estimate semantic similarity using six different methods: four holistic (summary-text, summary-summaries, summary-expert summaries, and pregraded-ungraded summary) and two componential (summary-sentence text and summary-main sentence text). A total of 390 Spanish middle and high school students (14–16 years old) and six experts read a narrative or expository text and later summarized it. The results support the viability of developing a computerized assessment tool using human judgments and LSA, although the correlation between human judgments and LSA was higher in the narrative text than in the expository, and LSA correlated more with human content ratings than with human coherence ratings. Finally, the holistic methods were found to be more reliable than the componential methods analyzed in this study.     

New algorithms assessing short summaries in expository texts using latent semantic analysis

In this study, we compared four expert graders with latent semantic analysis (LSA) to assess short summaries of an expository text. As is well known, there are technical difficulties for LSA to establish a good semantic representation when analyzing short texts. In order to improve the reliability of LSA relative to human graders, we analyzed three new algorithms by two holistic methods used in previous research (León, Olmos, Escudero, Cañas, &; Salmerón, 2006). The three new algorithms were (1) the semantic common network algorithm, an adaptation of an algorithm proposed by W. Kintsch (2001, 2002) with respect to LSA as a dynamic model of semantic representation; (2) a best-dimension reduction measure of the latent semantic space, selecting those dimensions that best contribute to improving the LSA assessment of summaries (Hu, Cai, Wiemer-Hastings, Graesser, &; McNamara, 2007); and (3) the Euclidean distance measure, used by Rehder et al. (1998), which incorporates at the same time vector length and the cosine measures. A total of 192 Spanish middle-grade students and 6 experts took part in this study. They read an expository text and produced a short summary. Results showed significantly higher reliability of LSA as a computerized assessment tool for expository text when it used a best-dimension algorithm rather than a standard LSA algorithm. The semantic common network algorithm also showed promising results.     

Latent semantic analysis cosines as a cognitive similarity measure: Evidence from priming studies

In distributional semantics models (DSMs) such as latent semantic analysis (LSA), words are represented as vectors in a high-dimensional vector space. This allows for computing word similarities as the cosine of the angle between two such vectors. In two experiments, we investigated whether LSA cosine similarities predict priming effects, in that higher cosine similarities are associated with shorter reaction times (RTs). Critically, we applied a pseudo-random procedure in generating the item material to ensure that we directly manipulated LSA cosines as an independent variable. We employed two lexical priming experiments with lexical decision tasks (LDTs). In Experiment 1 we presented participants with 200 different prime words, each paired with one unique target. We found a significant effect of cosine similarities on RTs. The same was true for Experiment 2, where we reversed the prime-target order (primes of Experiment 1 were targets in Experiment 2, and vice versa). The results of these experiments confirm that LSA cosine similarities can predict priming effects, supporting the view that they are psychologically relevant. The present study thereby provides evidence for qualifying LSA cosine similarities not only as a linguistic measure, but also as a cognitive similarity measure. However, it is also shown that other DSMs can outperform LSA as a predictor of priming effects.     

Identifying reading strategies using latent semantic analysis: comparing semantic benchmarks.

We explored methods of using latent semantic analysis (LSA) to identify reading strategies in students' self-explanations that are collected as part of a Web-based reading trainer. In this study, college students self-explained scientific texts, one sentence at a time. ISA was used to measure the similarity between the self-explanations and semantic benchmarks (groups of words and sentences that together represent reading strategies). Three types of semantic benchmarks were compared: content words, exemplars, and strategies. Discriminant analyses were used to classify global and specific reading strategies using the LSA cosines. All benchmarks contributed to the classification of general reading strategies, but the exemplars did the best in distinguishing subtle semantic differences between reading strategies. Pragmatic and theoretical concerns of using LSA are discussed.     

Estimating the effect of word predictability on eye movements in Chinese reading using latent semantic analysis and transitional probability

Latent semantic analysis (LSA) and transitional probability (TP), two computational methods used to reflect lexical semantic representation from large text corpora, were employed to examine the effects of word predictability on Chinese reading. Participants' eye movements were monitored, and the influence of word complexity (number of strokes), word frequency, and word predictability on different eye movement measures (first-fixation duration, gaze duration, and total time) were examined. We found influences of TP on first-fixation duration and gaze duration and of LSA on total time. The results suggest that TP reflects an early stage of lexical processing while LSA reflects a later stage.     

Use of latent semantic analysis for predicting psychological phenomena: Two issues and proposed solutions

Latent semantic analysis (LSA) is a computational model of human knowledge representation that approximates semantic relatedness judgments. Two issues are discussed that researchers must attend to when evaluating the utility of LSA for predicting psychological phenomena. First, the role of semantic relatedness in the psychological process of interest must be understood. LSA indices of similarity should then be derived from this theoretical understanding. Second, the knowledge base (semantic space) from which similarity indices are generated must contain “knowledge” that is appropriate to the task at hand. Proposed solutions are illustrated with data from an experiment in which LSA-based indices were generated from theoretical analysis of the processes involved in understanding two conflicting accounts of a historical event. These indices predict the complexity of subsequent student reasoning about the event, as well as hand-coded predictions generated from think-aloud protocols collected when students were reading the accounts of the event.     

儿童语言样本的分析技术

语言样本分析是评定儿童表达性语言能力发展的一个有效的广泛应用的方法,文章介绍了国外关于语言样本分析技术的发展情况,包括语言样本分析的程序、流行程度、语言样本的诱发方式,及其宏观结构和微观结构的分析指标;总结了国内在儿童语言样本分析领域的三方面的探索研究;未来的国内研究可以在宏观结构分析方面借鉴国外的经验,但在微观结构分析方面还需要基于汉语自身的特点,开发出适于中国儿童的语言样本分析指标。     

A method of scalogram analysis using summary statistics

A method of Guttman scalogram analysis is presented that does not involve sorting and rearranging the entries in the item response matrix. The method requires dichotomous items. Formulas are presented for estimating the reproducibility of the scale and estimating the expected value of the chance reproducibility. An index of consistency is suggested for evaluating the reproducibility. An illustrative example is presented in detail. The logical basis of the method is discussed. Finally, several methods are suggested for dealing with non-dichotomous items.Lois K. Anderson assisted the author materially in the many computations required for this paper. The research reported in this paper was supported in part by the Department of Economics and Social Sciences at M.I.T. and in part, jointly, by the Army, Navy and Air Force under contract with the Massachusetts Institute of Technology.     

Using context to build semantics

Latent semantic analysis (LSA) is a model of knowledge representation for words. It works by applying dimension reduction to local co-occurrence data from a large collection of documents after performing singular value decomposition on it. When the reduction is applied, the system forms condensed representations for the words that incorporate higher order associations. The higher order associations are primarily responsible for any semantic similarity between words in LSA. In this article, a memory model is described that creates semantic representations for words that are similar in form to those created by LSA. However, instead of applying dimension reduction, the model builds the representations by using a retrieval mechanism from a well-known account of episodic memory.     

语义空间的研究方法

对于语义空间的研究一直是认知心理学研究的一个热点。由于对词汇语义系统的不同观点,科学家们试图从不同的角度采用不同的方法来进行研究。目前,有代表性的语义空间研究方法主要有两种:潜在语义分析(LSA)和语言的多维空间类比(HAL)。潜在语义分析是指利用奇异值分解的方法来探索文章中潜在的语义关系的方法;语言的多维空间类比则是利用多维量表(MDS)的方法来提取语义信息。     

探测认知发展的非连续性:思路与方法

认知发展是连续性的还是非连续性的是发展心理学中一个重要理论问题.目前研究者逐渐认识到发展既有连续性的也有非连续性的,因而关键的问题是什么情况下是非连续性的,什么情况下是连续性的.该问题的回答与非连续性的检测标准有很大关系.本研究详细介绍了考察发展非连续性的三种思路和方法:量表图分析和多重任务法;Rasch模型分析和Saltus模型分析;突变理论的应用.最后,强调了影响发展非连续性问题探讨的一些因素.     

Social Representations of Multiculturalism: A Faceted Analysis

This paper reports a study that examines how Australians are responding to the policy of multiculturalism. The aim of the study was to define different social representations of the policy. Multiple scalogram analysis (MSA) yielded 3 facets of social representations of multiculturalism (active/passive, substantial/minimal, and threat/benefit). Using these facets, subjects were categorized into social representation groups. The results illustrate the methodological utility of using facet theory techniques to study social representations.     

Support of Content and Rhetorical Processes of Writing: Effects on the Writing Process and the Written Product

The effects of content and rhetorical prompts on writing process activities and the quality of written products were examined. We also examined the usefulness of latent semantic analysis (LSA; Landauer & Dumais, 1997)-a computational technique for representing the content of documents-as a tool for assessing texts. Participants used varied combinations of prompts designed to support content and rhetorical processes. In Experiment 1, content and rhetorical processes were supported only during composition. In Experiment 2, content and rhetorical processes were supported during domain learning and writing. Time spent in 3 writing activities (planning, drafting, revising) was measured, and professional writing instructors and LSA assessed text quality. Content prompts extended time spent writing and were related to improved text quality; rhetorical prompts demonstrated some influence on planning and global text quality only when presented during domain learning. In both experiments, LSA generated consistent judgments of writing quality that resembled human grading.     

Orienting and maintenance of gaze to facial expressions in social anxiety

In 2 experiments, the authors tested predictions from cognitive models of social anxiety regarding attentional biases for social and nonsocial cues by monitoring eye movements to pictures of faces and objects in high social anxiety (HSA) and low social anxiety (LSA) individuals. Under no-stress conditions (Experiment 1), HSA individuals initially directed their gaze toward neutral faces, relative to objects, more often than did LSA participants. However, under social-evaluative stress (Experiment 2), HSA individuals showed reduced biases in initial orienting and maintenance of gaze on faces (cf. objects) compared with the LSA group. HSA individuals were also relatively quicker to look at emotional faces than neutral faces but looked at emotional faces for less time, compared with LSA individuals, consistent with a vigilant-avoidant pattern of bias.