A pilot study in an application of text mining to learning system evaluation

Text mining concerns discovering and extracting knowledge from unstructured data. It transforms textual data into a usable, intelligible format that facilitates classifying documents, finding explicit relationships or associations between documents, and clustering documents into categories. Given a collection of survey comments evaluating the civil engineering learning system, text mining technique is applied to discover and extract knowledge from the comments. This research focuses on the study of a systematic way to apply a software tool, SAS Enterprise Miner, to the survey data. The purpose is to categorize the comments into different groups in an attempt to identify major concerns from the users or students. Each group will be associated with a set of key terms. This is able to assist the evaluators of the learning system to obtain the ideas from those summarized terms without the need of going through a potentially huge amount of data --Abstract, page iii

CORLEONE - Core Linguistic Entity Online Extraction

This report presents CORLEONE (Core Linguistic Entity Online Extraction) - a pool of loosely coupled general-purpose basic lightweight linguistic processing resources, which can be independently used to identify core linguistic entities and their features in free texts. Currently, CORLEONE consists of five processing resources: (a) a basic tokenizer, (b) a tokenizer which performs fine-grained token classification, (c) a component for performing morphological analysis, and (d) a memory-efficient database-like dictionary look-up component, and (e) sentence splitter. Linguistic resources for several languages are provided. Additionally, CORLEONE includes a comprehensive library of string distance metrics relevant for the task of name variant matching. CORLEONE has been developed in the Java programming language and heavily deploys state-of-the-art finite-state techniques. Noteworthy, CORLEONE components are used as basic linguistic processing resources in ExPRESS, a pattern matching engine based on regular expressions over feature structures and in the real-time news event extraction system, which were developed by the Web Mining and Intelligence Group of the Support to External Security Unit of IPSC. This report constitutes an end-user guide for COLREONE and provides scientifically interesting details of how it was implemented.JRC.G.2-Support to external securit