Peter W. Foltz

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.

The measurement of textual coherence with latent semantic analysis

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.

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.

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 indexing for information filtering

Latent Semantic Indexing (LSI) is an information retrieval method that organizes information into a semantic structure that takes advantage of some of the implicit higher-order associations of words with text objects. The resulting structure reflects the major associative patterns in the data while ignoring some of the smaller variations that may be due to idiosyncrasies in the word usage of individual documents. This permits retrieval based on the “latent” semantic content of the documents rather than just on keyword matches. This paper evaluates using LSI for filtering information such as Netnews articles based on a model of user preferences for articles. Users judged articles on how interesting they were and based on these judgements, LSI predicted whether new articles would be judged interesting. LSI improved prediction performance over keyword matching an average of 13% and showed a 26% improvement in precision over presenting articles in the order received. The results indicate that user preferences for articles tend to cluster based on the semantic similarities between articles.

Some Promising Results of Communication-Based Automatic Measures of Team Cognition

Some have argued that the most appropriate measure of team cognition is a holistic measure directed at the entire team. In particular, communication data are useful for measuring team cognition because of the holistic nature of the data, and because of the connection between communication and declarative cognition. In order to circumvent the logistic difficulties of communication data, the present paper proposes several relatively automatic methods of analysis. Four data types are identified, with low-level physical data vs. content data being one dimension, and sequential vs. static data being the other. Methods addressing all four of these data types are proposed, with the exception of static physical data. Latent Semantic Analysis is an automatic method used to assess content, either statically or sequentially. PRONET is useful to address either physical or content-based sequential data, and we propose CHUMS to address sequential physical data. The usefulness of each method to predict team performance data is assessed.

An automated method to analyze language use in patients with schizophrenia and their first-degree relatives

Communication disturbances are prevalent in schizophrenia, and since it is a heritable illness these are likely present - albeit in a muted form - in the relatives of patients. Given the time-consuming, and often subjective nature of discourse analysis, these deviances are frequently not assayed in large scale studies. Recent work in computational linguistics and statistical-based semantic analysis has shown the potential and power of automated analysis of communication. We present an automated and objective approach to modeling discourse that detects very subtle deviations between probands, their first-degree relatives and unrelated healthy controls. Although these findings should be regarded as preliminary due to the limitations of the data at our disposal, we present a brief analysis of the models that best differentiate these groups in order to illustrate the utility of the method for future explorations of how language components are differentially affected by familial and illness related issues.

Evaluation of Latent Semantic Analysis-Based Measures of Team Communications Content

Team process is thought to mediate team member inputs and team performance. Among the team behaviors identified as process variables, team communications have been widely studied. We view team communications as a team behavior and also as team information processing, or team cognition. Within the context of a Predator Uninhabited Air Vehicle (UAV) synthetic task, we have developed several methods of communications content assessment based on Latent Semantic Analysis (LSA). These methods include: Communications Density (CD) which is the average task relevance of a teams communications, Lag Coherence (LC) which measures task-relevant topic shifting over UAV missions, and Automatic Tagging (AT) which categorizes team communications. Each method is described in detail. CD and LC are related to UAV team performance. AT-human is comparable to human-human agreement on content coding. The results are promising for the assessment of teams based on LSA applied to communication content.

Latent semantic variables are associated with formal thought disorder and adaptive behavior in older inpatients with schizophrenia

INTRODUCTION Formal thought disorder is a hallmark feature of schizophrenia in which disorganized thoughts manifest as disordered speech. A dysfunctional semantic system and a disruption in executive functioning have been proposed as possible mechanisms for formal thought disorder and verbal fluency impairment. Traditional rating scales and neuropsychological test scores might not be sensitive enough to distinguish among types of semantic impairments. This has lead to the proposed used of a natural language processing technique, Latent Semantic Analysis (LSA), which offers improved semantic sensitivity. METHOD In this study, LSA, a computational, vector-based text analysis technique to examine the contribution of vector length, an LSA measure related to word unusualness and cosines between word vectors, an LSA measure of semantic coherence to semantic and phonological fluency, disconnectedness of speech, and adaptive functioning in 165 older inpatients with schizophrenia. RESULTS In stepwise regressions word unusualness was significantly associated with semantic fluency and phonological fluency, disconnectedness in speech, and impaired functioning, even after considering the contribution of premorbid cognition, positive and negative symptoms, and demographic variables. CONCLUSIONS These findings support the utility of LSA in examining the contribution of coherence to thought disorder and the its relationship with daily functioning. Deficits in verbal fluency may be an expression of underlying disorganization in thought processes.

Automated team discourse annotation and performance prediction using LSA

We describe two approaches to analyzing and tagging team discourse using Latent Semantic Analysis (LSA) to predict team performance. The first approach automatically categorizes the contents of each statement made by each of the three team members using an established set of tags. Performance predicting the tags automatically was 15% below human agreement. These tagged statements are then used to predict team performance. The second approach measures the semantic content of the dialogue of the team as a whole and accurately predicts the teams performance on a simulated military mission.