Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping

The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.

Between the input and the acquisition lies the shadow

Acquisition by Processing Theory (APT) is a unified account of language processing and learning that encompasses both L1 and L2 acquisition. Bold in aim and broad in scope, the proposal offers parsimony and comprehensiveness, both highly desirable in a theory of language acquisition. However, the sweep of the proposal is accompanied by an economy of description that makes it difficult to evaluate the validity of key learning claims, or even how literally they are to be interpreted. Two in particular deserve comment; the first concerns the learning mechanisms responsible for adding new L2 grammatical information, and the second the theoretical and empirical status of the activation concept used in the model.

Leximancer concept mapping of patient case studies

Quantitative databases are limited to information identified as important by their creators, while databases containing natural language are limited by our ability to analyze large unstructured bodies of text. Leximancer is a tool that uses semantic mapping to develop concept maps from natural language. We have applied Leximancer to educational based pathology case notes to demonstrate how real patient records or databases of case studies could be analyzed to identify unique relationships. We then discuss how such analysis could be used to conduct quantitative analysis from databases such as the Coronary Heart Disease Database.

The performance of South African English first language child speakers on a "low linguistically loaded" central auditory processing test protocol

The lack of standardized tests of central auditory processing disorder (CAPD) in South Africa (SA) led to the formation of a SA CAPD Taskforce, and the interim development of a "low linguistically loaded" CAPD test protocol using test recordings from the 'Tonal and Speech Materials for Auditory Perceptual Assessment Disc 2.0'. This study inferentially compared the performance of 16 SA English first, and 16 SA English second, language adult speakers on this test protocol, and descriptively compared their performances to previously published American normative data. Comparisons between the SA English first and second language speakers showed a poorer right ear performance (p < .05) by the second language speakers on the two-pair dichotic digits test only. Equivalent performances (p < .05) were observed on the left ear performance on the two pair dichotic digits test, and the frequency patterns test, the duration patterns test, the low-pass filtered speech test, the 45% time compressed speech test, the speech masking level difference test, and the consonant vowel consonant (CVC) binaural fusion test. Comparisons between the SA English and the American normative data showed many large differences (up to 37.1% with respect to predicted pass criteria as calculated by mean-2SD cutoffs), with the SA English speakers performing both better and worse depending on the test involved. As a result, the American normative data was not considered appropriate for immediate use as normative data in SA. Instead, the preliminary data provided in this study was recommended as interim normative data for both SA English first and second language adult speakers, until larger scale SA normative data can be obtained.