Positioning for conceptual development using latent semantic analysis

With increasing opportunities to learn online, the problem of positioning learners in an educational network of content offers new possibilities for the utilisation of geometry-based natural language processing techniques. In this article, the adoption of latent semantic analysis (LSA) for guiding learners in their conceptual development is investigated. We propose five new algorithmic derivations of LSA and test their validity for positioning in an experiment in order to draw back conclusions on the suitability of machine learning from previously accredited evidence. Special attention is thereby directed towards the role of distractors and the calculation of thresholds when using similarities as a proxy for assessing conceptual closeness. Results indicate that learning improves positioning. Distractors are of low value and seem to be replaceable by generic noise to improve threshold calculation. Furthermore, new ways to flexibly calculate thresholds could be identified

Indonesian News Article Summarization using Latent Semantic Analysis (LSA)

The number of news articles with various volumes is increasing, so they need to be summarized. This is to find important information in Indonesian language news articles. For this purposed, a lot of research has been done to build an automatic summation system. Steinberger et.al. propose the sentence selection method in summarizing text based on the Latent Semantic Analysis (LSA) method. The sentence selection was conducted based on the value of the concept that the first concept was considered to have the highest importance, so there was a possibility that sentences that the unimportant sentences were selected. This results in summaries containing incoherent information. This study proposed the sentence selection method based on the relatedness between concepts and sentences and referring to the structure of writing news articles. The experimental results showed that the proposed sentence selection was able to summarize the text well and achieved better performance than the result of Steinberger et.al. sentence selection method. In average the value of F-Measure increases by 42% for 10% compression rates, 39% for 30% compression rates, and increases by 24% in a 50% compression rate

Comparison of Latent Semantic Analysis and Probabilistic Latent Semantic Analysis for Documents Clustering

In this paper we compare usefulness of statistical techniques of dimensionality reduction for improving clustering of documents in Polish. We start with partitional and agglomerative algorithms applied to Vector Space Model. Then we investigate two transformations: Latent Semantic Analysis and Probabilistic Latent Semantic Analysis. The obtained results showed advantage of Latent Semantic Analysis technique over probabilistic model. We also analyse time and memory consumption aspects of these transformations and present runtime details for IBM BladeCenter HS21 machine

The Learning Grid and E-Assessment using Latent Semantic Analysis

E-assessment is an important component of e-learning and e-qualification. Formative and summative assessment serve different purposes and both types of evaluation are critical to the pedagogicalprocess. While students are studying, practicing, working, or revising, formative assessment provides direction, focus, and guidance. Summative assessment provides the means to evaluate a learner's achievement and communicate that achievement to interested parties. Latent Semantic Analysis (LSA) is a statistical method for inferring meaning from a text. Applications based on LSA exist that provide both summative and formative assessment of a learner's work. However, the huge computational needs are a major problem with this promising technique. This paper explains how LSA works, describes the breadth of existing applications using LSA, explains how LSA is particularly suited to e-assessment, and proposes research to exploit the potential computational power of the Grid to overcome one of LSA's drawbacks