Manuel de Buenaga Rodríguez

Using and Evaluating User Directed Summaries to Improve Information Access

Textual information available has grown so much as to make necessary to study new techniques that assist users in information access (IA). In this paper, we propose utilizing a user directed summarization system in an IA setting for helping users to decide about document relevance. The summaries are generated using a sentence extraction method that scores the sentences performing some heuristics employed successfully in previous works (keywords, title and location). User modeling is carried out exploiting users query to an IA system and expanding query terms using WordNet. We present an objective and systematic evaluation method oriented to measure the summary effectiveness in two IA significant tasks: ad hoc retrieval and relevance feedback. Results obtained prove our initial hypothesis, i.e., user adapted summaries are a useful tool assisting users in an IA context.

The role of word sense disambiguation in automated text categorization

Automated Text Categorization has reached the levels of accuracy of human experts. Provided that enough training data is available, it is possible to learn accurate automatic classifiers by using Information Retrieval and Machine Learning Techniques. However, performance of this approach is damaged by the problems derived from language variation (specially polysemy and synonymy). We investigate how Word Sense Disambiguation can be used to alleviate these problems, by using two traditional methods for thesaurus usage in Information Retrieval, namely Query Expansion and Concept Indexing. These methods are evaluated on the problem of using the Lexical Database WordNet for text categorization, focusing on the Word Sense Disambiguation step involved. Our experiments demonstrate that rather simple dictionary methods, and baseline statistical approaches, can be used to disambiguate words and improve text representation and learning in both Query Expansion and Concept Indexing approaches.

Big Data Processing Using Wearable Devices for Wellbeing and Healthy Activities Promotion

The aging population and economic crisis specially in developed countries have as a consequence the reduction in funds dedicated to healthcare, is then desirable to optimize the costs of public and private healthcare systems reducing the affluence of chronic and dependent people to care centers; promoting healthy lifestyle and activities can allow people to avoid chronic diseases as for example hypertension. In this paper we describe a system for promoting an active and healthy lifestyle for people and to recommend with guidelines and valuable information about their habits. The proposed system is being developed around the Big Data parading using bio-signals sensors and machine learning algorithms for recommendations.

A User Model Based on Content Analysis for the Intelligent Personalization of a News Service

In this paper we present a methodology designed to improve the intelligent personalization of news services. Our methodology integrates textual content analysis tasks to achieve an elaborate user model, which represents separately short-term needs and long-term multi-topic interests. The characterization of users interests includes his preferences about content, using a wide coverage and non-specific-domain classification of topics, and structure (newspaper sections). The application of implicit feedback allows a proper and dynamic personalization.

Chapter 7 Web Content Filtering

Abstract Across the years, Internet has evolved from an academic network to a truly communication medium, reaching impressive levels of audience and becoming a billionaire business. Many of our working, studying, and entertainment activities are nowadays overwhelmingly limited if we get disconnected from the net of networks. And of course, with the use comes abuse. The World Wide Web features a wide variety of content that are harmful for children or just inappropriate in the workplace. Web filtering and monitoring systems have emerged as valuable tools for the enforcement of suitable usage policies. These systems are routinely deployed in corporate, library, and school networks, and contribute to detect and limit Internet abuse. Their techniques are increasingly sophisticated and effective, and their development is contributing to the advance of the state of the art in a number of research fields, like text analysis and image processing. In this chapter, we review the main issues regarding Web content filtering, including its motivation, the main operational concerns and techniques used in filtering tools’ development, their evaluation and security, and a number of singular projects in this field.

Information Retrieval by Means of Word Sense Disambiguation

The increasing problem of information overload can be reduced by the improvement of information access tasks like Information Retrieval. Relevance Feedback plays a key role in this task, and is typically based only on the information extracted from documents judged by the user for a given query. We propose to make use of a thesaurus to complement this information to improve RF. This must be done by means of a Word Sense Disambiguation process that correctly identifies the suitable information from the thesaurus WORDNET. The results of our experiments show that the utilisation of a thesaurus requires Word Sense Disambiguation, and that with this process, Relevance Feedback is substantially improved.

Healthy and wellbeing activities' promotion using a Big Data approach.

The aging population and economic crisis specially in developed countries have as a consequence the reduction in funds dedicated to health care; it is then desirable to optimize the costs of public and private healthcare systems, reducing the affluence of chronic and dependent people to care centers; promoting healthy lifestyle and activities can allow people to avoid chronic diseases as for example hypertension. In this article, we describe a system for promoting an active and healthy lifestyle for people and to recommend with guidelines and valuable information about their habits. The proposed system is being developed around the Big Data paradigm using bio-signal sensors and machine-learning algorithms for recommendations.