Diana Pérez

An approach for automatic generation of adaptive hypermedia in education with multilingual knowledge discovery techniques

This work describes a framework that combines techniques from Adaptive Hypermedia and Natural Language processing in order to create, in a fully automated way, on-line information systems from linear texts in electronic format, such as textbooks. The process is divided into two steps: an off-line processing step, which analyses the source text, and an on-line step, which executes when a user connects to the system with a web browser, moment at which the contents and hyperlinks are generated. The framework has been implemented as the Welkin system, which has been used to build three adaptive on-line information sites in a quick and easy way. Some controlled experiments have been performed with real users aimed to provide positive feedback on the implementation of the system.

Automatic Assessment of Open Ended Questions with a Bleu -Inspired Algorithm and Shallow NLP

This paper compares the accuracy of several variations of the Bleu algorithm when applied to automatically evaluating student essays. The different configurations include closed-class word removal, stemming, two baseline word-sense disambiguation procedures, and translating the texts into a simple semantic representation. We also prove empirically that the accuracy is kept when the student answers are translated automatically. Although none of the representations clearly outperform the others, some conclusions are drawn from the results.

Using bleu-like algorithms for the automatic recognition of entailment

The Bleu algorithm has been used in many different fields. Another possible application is the automatic recognition of textual entailment. Bleu works at the lexical level, by comparing a candidate text with several reference texts in order to calculate how close the candidate text is to the references. In this case, the candidate is the text part of the entailment and the hypothesis is the unique reference. The algorithm achieves an accuracy of around 50%. Moreover, in this paper we explore the application of Bleu-like algorithms, finding that they can reach an accuracy of around 56%, which proves its possible use as a baseline for the task of recognizing entailment.

Welkin: Automatic Generation of Adaptive Hypermedia Sites with NLP Techniques

The demonstration shows the system Welkin, a multilingual system that analyses one or several source texts with a cascade of linguistic-processing modules, including syntactic analyses, text identification and text classification techniques, in order to fill in a database of information about it. That information is later used to generate on-the-fly adaptive on-line information sites according to some user profiles.

PNEPs, NEPs for Context Free Parsing: Application to Natural Language Processing

This work tests the suitability of NEPs to parse languages. We propose PNEP, a simple extension to NEP, and a procedure to translate a grammar into a PNEP that recognizes the same language. These parsers based on NEPs do not impose any additional constrain to the structure of the grammar, which can contain all kinds of recursive, lambda or ambiguous rules. This flexibility makes this procedure specially suited for Natural Languge Processing (NLP). In a first proof with a simplified English grammar, we got a performance (a linear time complexity) similar to that of the most popular syntactic parsers in the NLP area (Early and its derivatives). All the possible derivations for ambiguous grammars were generated.