Darrell Laham

An introduction to latent semantic analysis

Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual‐usage meaning of words by statistical computations applied to a large corpus of text (Landauer & Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSAs reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word‐word and passage‐word lexical priming data; and, as reported in 3 following articles in this issue, it accurately estimates passage coherence, learnability of passages by individual students, and the quality and quantity of knowledge contained in an essay.

Learning from text: Matching readers and texts by latent semantic analysis

This study examines the hypothesis that the ability of a reader to learn from text depends on the match between the background knowledge of the reader and the difficulty of the text information. Latent Semantic Analysis (LSA), a statistical technique that represents the content of a document as a vector in high‐dimensional semantic space based on a large text corpus, is used to predict how much readers will learn from texts based on the estimated conceptual match between their topic knowledge and the text information. Participants completed tests to assess their knowledge of the human heart and circulatory system, then read one of four texts that ranged in difficulty from elementary to medical school level, then completed the tests again. Results show a nonmonotonic relation in which learning was greatest for texts that were neither too easy nor too difficult. LSA proved as effective at predicting learning from these texts as traditional knowledge assessment measures. For these texts, optimal assignment o...

Using Latent Semantic Analysis To Assess Knowledge: Some Technical Considerations.

In another article (Wolfe et al., 1998/this issue) we showed how Latent Semantic Analysis (LSA) can be used to assess student knowledge—how essays can be graded by LSA and how LSA can match students with appropriate instructional texts. We did this by comparing an essay written by a student with one or more target instructional texts in terms of the cosine between the vector representation of the students essay and the instructional text in question. This simple method was effective for the purpose, but questions remain about how LSA achieves its results and how the results might be improved. Here, we address four such questions: (a) What role does the use of technical vocabulary play? (b) how long should the student essays be? (c) is the cosine the optimal measure of semantic relatedness? and (d) how does one deal with the directionality of knowledge in the high‐dimensional space?

Automated Speech Recognition for Modeling Team Performance

While team tasks provide a wealth of data on individual and team performance, techniques for modeling team communication can be quite effortful and time-consuming. Automated techniques of analyzing team discourse provide the promise of quickly judging team performance and permitting feedback to teams both in training and in operations. In previous research, techniques using Latent Semantic Analysis (LSA) have proven successful for analyzing team transcripts. However, converting the audio discourse into transcripts often requires hand transcription. In this work, we describe applying automated speech recognition (ASR) to team transcripts and using the output of the ASR to predict overall team performance. Results indicate that ASR can be used in conjunction with semantic methods of modeling team communication to provide accurate predictions of performance. The work has potential for assisting operators in the performance of their tasks because it can “listen” and in real-time evaluate free-form verbal communication from a variety of sources.