Raquel Martínez Unanue

Learning a taxonomy from a set of text documents

We present a methodology for learning a taxonomy from a set of text documents that each describes one concept. The taxonomy is obtained by clustering the concept definition documents with a hierarchical approach to the Self-Organizing Map. In this study, we compare three different feature extraction approaches with varying degree of language independence. The feature extraction schemes include fuzzy logic-based feature weighting and selection, statistical keyphrase extraction, and the traditional tf-idf weighting scheme. The experiments are conducted for English, Finnish, and Spanish. The results show that while the rule-based fuzzy logic systems have an advantage in automatic taxonomy learning, taxonomies can also be constructed with tolerable results using statistical methods without domain- or style-specific knowledge.

Using Fuzzy Logic to Leverage HTML Markup for Web Page Representation

The selection of a suitable document representation approach plays a crucial role in the performance of a document clustering task. Being able to pick out representative words within a document can lead to substantial improvements in document clustering. In the case of web documents, the HTML markup that defines the layout of the content provides additional structural information that can be further exploited to identify representative words. In this paper, we introduce a fuzzy term weighing approach that makes the most of the HTML structure for document clustering. We set forth and build on the hypothesis that a good representation can take advantage of how humans skim through documents to extract the most representative words. The authors of web pages make use of HTML tags to convey the most important message of a web page through page elements that attract the readers’ attention, such as page titles or emphasized elements. We define a set of criteria to exploit the information provided by these page elements, and introduce a fuzzy combination of these criteria that we evaluate within the context of a web page clustering task. Our proposed approach, called abstract fuzzy combination of criteria (AFCC), can adapt to datasets whose features are distributed differently, achieving good results compared with other similar fuzzy logic based approaches and TF-IDF across different datasets.

Estudio preliminar de la anotación automática de códigos CIE-10 en informes de alta hospitalarios

En la actualidad, la cantidad de recursos que se destinan a la codificacion de informes medicos es enorme. Con la reciente implantacion del estandar CIE-10 en el sistema sanitario espanol se acrecientan las dificultades, ya que se incrementa el numero de posibles codigos CIE por cada informe, disponiendo de una escasa cantidad de datos generados y codificados. En este articulo se describen los retos que plantea esta tarea y se propone una primera aproximacion de combinacion de tecnicas para implantar un sistema capaz de, dado un informe, recomendar automaticamente codigos CIE-10 a los codificadores.