José Carlos Cortizo

Introduction to the Special Issue: Mining Social Media

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Concept indexing for automated text categorization

In this paper we explore the potential of concept indexing with WordNet synsets for Text Categorization, in comparison with the traditional bag of words text representation model. We have performed a series of experiments in which we also test the possibility of using simple yet robust disambiguation methods for concept indexing, and the effectiveness of stoplist-filtering and stemming on the SemCor semantic concordance. Results are not conclusive yet promising.

Overview of the third international workshop on search and mining user-generated contents

This overview introduces the aim of the SMUC 2010 workshop, as well as the list of papers presented in the workshop.

Testing concept indexing in crosslingual medical text classification

MetaMap is an online application that allows mapping text to UMLS Metathesaurus concepts, which is very useful for interoperability among different languages and systems within the biomedical domain. MetaMap Transfer (MMTx) is a Java program that makes MetaMap available to biomedical researchers in controlled, configurable environment. Currently there is no Spanish version of MetaMap, which difficult the use of UMLS Metathesaurus to extract concepts from Spanish biomedical texts. Developing a Spanish version of MetaMap would be a huge task, since there has been a lot of work supporting the English version for the last sixteen years. Our ongoing research is mainly focused on using biomedical concepts for crosslingual text classification. In this context the use of concepts instead of bag of words representation allows us to face text classification tasks abstracting from the language. In this paper we show our experiments on combining automatic translation techniques with the use of biomedical ontologies to produce an English text that can be processed by MMTx in order to extract concepts for text classification.

In the development of a spanish metamap

MetaMap is an online application that allows mapping text to UMLS Metathesaurus concepts, which is very useful interoperability among different languages and systems within the biomedical domain. MetaMap Transfer (MMTx) is a Java program that makes MetaMap available to biomedical researchers. Currently there is no Spanish version of MetaMap, which difficults the use of UMLS Metathesaurus to extract concepts from Spanish biomedical texts. Our ongoing research is mainly focused on using biomedical concepts for cross-lingual text classification and retrieval [3]. In this context the use of concepts instead of bag of words representation allows us to face text classification tasks abstracting from the language [4]. In this paper we evaluate the possibility of combining automatic translation techniques with the use of biomedical ontologies to produce an English text that can be processed by MMTx.

Wrapping the Naive Bayes classifier to relax the effect of dependences

The Naive Bayes Classifier is based on the (unrealistic) assumption of independence among the values of the attributes given the class value. Consequently, its effectiveness may decrease in the presence of interdependent attributes. In spite of this, in recent years, Naive Bayes classifier is worked for a privilege position due to several reasons [1]. We present DGW (Dependency Guided Wrapper), a wrapper that uses information about dependences to transform the data representation to improve the Naive Bayes classification. This paper presents experiments comparing the performance and execution time of 12 DGW variations against 12 previous approaches, as constructive induction of cartesian product attributes, and wrappers that perform a search for optimal subsets of attributes. Experimental results show that DGW generates a new data representation that allows the Naive Bayes to obtain better accuracy more times than any other wrapper tested. DGW variations also obtain the best possible accuracy more often than the state of the art wrappers while often spending less time in the attribute subset search process.

Multi criteria wrapper improvements to naive bayes learning

Feature subset selection using a wrapper means to perform a search for an optimal set of attributes using the Machine Learning Algorithm as a black box. The Naive Bayes Classifier is based on the assumption of independence among the values of the attributes given the class value. Consequently, its effectiveness may decrease when the attributes are interdependent. We present FBL, a wrapper that uses information about dependencies to guide the search for the optimal subset of features and we use the Naive Bayes Classifier as the black-box Machine Learning algorithm. Experimental results show that FBL allows the Naive Bayes Classifier to achieve greater accuracies, and that FBL performs better than other classical filters and wrappers.

Introduction to the Special Section on Search and Mining User-Generated Content

The primary goal of this special section of ACM Transactions on Intelligent Systems and Technology is to foster research in the interplay between Social Media, Data/Opinion Mining and Search, aiming to reflect the actual developments in technologies that exploit user-generated content.

Building a Spanish MMTx by Using Automatic Translation and Biomedical Ontologies

The use of domain ontologies is becoming increasingly popular in Medical Natural Language Processing Systems. A wide variety of knowledge bases in multiple languages has been integrated into the Unified Medical Language System (UMLS) to create a huge knowledge source that can be accessed with diverse lexical tools. MetaMap (and its java version MMTx) is a tool that allows extracting medical concepts from free text, but currently there not exists a Spanish version. Our ongoing research is centered on the application of biomedical concepts to cross-lingual text classification, what makes it necessary to have a Spanish MMTx available. We have combined automatic translation techniques with biomedical ontologies and the existing English MMTx to produce a Spanish version of MMTx. We have evaluated different approaches and applied several types of evaluation according to different concept representations for text classification. Our results prove that the use of existing translation tools such as Google Translate produce translations with a high similarity to original texts in terms of extracted concepts.

On the Future of Mobile Phones as the Heart of Community-Built Databases

In retrospect, 10 years ago, we would not have imagined ourselves uploading or consuming high-quality videos via the Web, contributing to an online encyclopedia written by millions of users around the world or instantly sharing information with our friends and colleagues using an online platform that allows us to manage our contacts. And the Web is still evolving and what seemed to be science fiction then would become reality within 5–10 years. Nowadays, the Mobile Web concept is still an immature prototype of what will be in a few years’ time, but it represents a giant industry (it is expected that some five billion people will be using mobile/cellular phones in 2010) with even greater possibilities in the future. In this paper, we examine the possible future of mobile devices as the heart of community-built databases. The mobile devices characteristics, as both current and future features, will allow them to have a very relevant role not only as interfaces to community-driven databases, but also as platforms where applications using data from community-driven databases will be running, or even as distributed databases where users can have better control of relevant data they are contributing to those databases.