José Luis Martínez-Fernández

Combining textual and visual features for image retrieval

This paper presents the approaches used by the MIRACLE team to image retrieval at ImageCLEF 2005. Text-based and content-based techniques have been tested, along with combination of both types of methods to improve image retrieval. The text-based experiments defined this year try to use semantic information sources, like thesaurus with semantic data or text structure. On the other hand, content-based techniques are not part of the main expertise of the MIRACLE team, but multidisciplinary participation in all aspects of information retrieval has been pursued. We rely on a publicly available image retrieval system (GIFT 4) when needed.

Automatic Keyword Extraction for News Finder

Newspapers are one of the most challenging domains for information retrieval systems: new articles appear everyday written in different languages, with multimedia contents and the news repositories may be updated in a matter of hours so information extraction is crucial to the metadata contents of the news. Further approaches of ”smart retrieval” have to cope with multimedia and multilingual features as well as have to obtain really good precision features in order to reach a high degree of user satisfaction with the retrieved documents. The paper focus is the description of the automatic keyword extraction (AKE) process for news characterization that uses several linguistic techniques to improve the current state of the text-based information retrieval. The first prototype implemented focusing in the AKE process (www.omnipaper.org) is described and some relevant performance features are included. Finally, some conclusions and comments are given regarding the role of the linguistic engineering in the web era.

MIRACLE approach to ImageCLEF 2004: merging textual and content-based image retrieval

This paper presents the image retrieval techniques tested by the MIRACLE (Multilingual Information RetrievAl for the CLEf campaign) research group as part of the ImageCLEF 2004 initiative. Two main lines of research continuing the past years experiments were considered: the application of linguistic techniques to improve retrieval performance and the combination of textual and content-based image retrieval.

MIRACLE's hybrid approach to bilingual and monolingual information retrieval

The main goal of the bilingual and monolingual participation of the MIRACLE team in CLEF 2004 was to test the effect of combination approaches on information retrieval. The starting point was a set of basic components: stemming, transformation, filtering, generation of n-grams, weighting and relevance feedback. Some of these basic components were used in different combinations and order of application for document indexing and for query processing. A second order combination was also tested, mainly by averaging or selective combination of the documents retrieved by different approaches for a particular query.

A real time Named Entity Recognition system for Arabic text mining

Arabic is the most widely spoken language in the Arab World. Most people of the Islamic World understand the Classic Arabic language because it is the language of the Qur’an. Despite the fact that in the last decade the number of Arabic Internet users (Middle East and North and East of Africa) has increased considerably, systems to analyze Arabic digital resources automatically are not as easily available as they are for English. Therefore, in this work, an attempt is made to build a real time Named Entity Recognition system that can be used in web applications to detect the appearance of specific named entities and events in news written in Arabic. Arabic is a highly inflectional language, thus we will try to minimize the impact of Arabic affixes on the quality of the pattern recognition model applied to identify named entities. These patterns are built up by processing and integrating different gazetteers, from DBPedia (http://dbpedia.org/About, 2009) to GATE (A general architecture for text engineering, 2009) and ANERGazet (http://users.dsic.upv.es/grupos/nle/?file=kop4.php).

MIRACLE at ImageCLEFphoto 2007: Evaluation of Merging Strategies for Multilingual and Multimedia Information Retrieval

This paper describes the participation of MIRACLE research consortium at the ImageCLEF Photographic Retrieval task of ImageCLEF 2007. For this campaign, the main purpose of our experiments was to thoroughly study different merging strategies, i.e. methods of combination of textual and visual retrieval techniques. Whereas we have applied all the well known techniques which had already been used in previous campaigns, for both textual and visual components of the system, our research has primarily focused on the idea of performing all possible combinations of those techniques in order to evaluate which ones may offer the best results and analyze if the combined results may improve (in terms of MAP) the individual ones.

A Preliminary Approach to the Automatic Extraction of Business Rules from Unrestricted Text in the Banking Industry

This paper addresses the problem of extracting formal statements, in the form of business rules, from free text descriptions of financial products or services. This automatic process is integrated in the banking software factory, permitting business analysts the formal specification, direct implementation and fast deployment of new products. This system is fully integrated with the typical software methodologies and architectures used in the banking industry for conventional development of backoffice or online applications.

MIRACLE’s cross-lingual question answering experiments with spanish as a target language

Our second participation in CLEF-QA consited in six runs with Spanish as a target language. The source languages were Spanish, English an Italian. miraQA uses a simple representation of the question that is enriched with semantic information like typed Named Entities. Runs used different strategies for answer extraction and selection, achieving at best a 25’5% accuracy. The analysis of the errors suggests that improvements in answer selection are the most critical.

Towards an Improvement of Software Development Processes through Standard Business Rules

The automation of software development processes is a desirable goal of current software companies which would lead to a cost reduction in software production. This automation is the backbone of approaches such as Model Driven Architecture (MDA) or Software Factories. This paper proposes the use of standard Business Rules (using Rules Interchange Format, RIF) to specify application functionality along with a platform to produce automatic implementations for them. The novelty of this proposal is to introduce Business Rules at all levels of MDA architecture in a software development process, providing a supporting tool where production Business Rules are considered at every abstraction level. Production Business Rules are represented through standard languages, rule engine vendor independence is assured via automatic transformation between rule languages, and Business Rules reuse is made possible. The objective is to get the development of production Business Rules closer to non-technical people involved in the software development process through the use of natural language processing approaches, automatic transformations among models and semantic web languages such as Ontology Web Language (OWL).

MIRACLE at WebCLEF 2005: combining web specific and linguistic information

This paper describes MIRACLE approach to WebCLEF. A set of independent indexes was constructed for each top level domain of the EuroGOV collection. Each index contains information extracted from the document, like URL, title, keywords, detected named entities or HTML headers. These indexes are queried to obtain partial document rankings, which are combined with various relative weights to test the value of each index. The final aim is to identify which index (or combination of them) is more relevant for a retrieval task, avoiding the construction of a full-text index.