Miguel A. García-Cumbreras

Integrating MeSH Ontology to Improve Medical Information Retrieval

This paper describes the SINAI team participation in the ImageCLEFmed campaign. The SINAI research group has participated in the multilingual image retrieval subtask. The experiments accomplished are based on the integration of specific knowledge in the topics. We have used the MeSH ontology to expand the queries. The expansion consists in searching terms from the topic query in the MeSH ontology in order to add similar terms. We have processed the set of collections using Information Gain (IG) in the same way as in ImageCLEFmed 2006. In our experiments mixing visual and textual information we obtain better results than using only textual information. The weigth of the textual information is very strong in this mixed strategy. In the experiments with a low textual weight, the use of IG improves the results obtained.

Pessimists and optimists: Improving collaborative filtering through sentiment analysis

This work presents a novel application of Sentiment Analysis in Recommender Systems by categorizing users according to the average polarity of their comments. These categories are used as attributes in Collaborative Filtering algorithms. To test this solution a new corpus of opinions on movies obtained from the Internet Movie Database (IMDb) has been generated, so both ratings and comments are available. The experiments stress the informative value of comments. By applying Sentiment Analysis approaches some Collaborative Filtering algorithms can be improved in rating prediction tasks. The results indicate that we obtain a more reliable prediction considering only the opinion text (RMSE of 1.868), than when apply similarities over the entire user community (RMSE of 2.134) and sentiment analysis can be advantageous to recommender systems.

Comparing Several Textual Information Retrieval Systems for the Geographical Information Retrieval Task

This paper presents a comparison between three different Information Retrieval (IR) systems employed in a particular Geographical Information Retrieval (GIR) system, the GeoUJA IR, a GIR architecture developed by the SINAI research group. It could be interesting and useful for determining which of the most used IR systems works better in GIR task. In the experiments, we have used the Lemur, Terrier and Lucene search engines using mono and bilingual queries. We present baseline cases, without applying any external processes, such as query expansion or filtering. In addition, we have used the default settings of each IR system. Results show that Lemur works better using monolingual queries and Terrier works better using the bilingual ones.

Otium: A web based planner for tourism and leisure

This paper introduces the Otiŭm planner system for scheduling of leisure tasks in tourism. This novel service allows users to create their own agenda of activities within specified dates. Activities are selected from a list of recommended events according to last selected events, user preferences and other parameters. The proposed restrictions on the recommendation procedure have been found to capture static and dynamic user context. The recommendation function is linear and shows low computational cost. The events are extracted from web sources with almost no human manipulation, so the recommender is always showing new and recent events. The Ajax-based web interface eases the creation of the final plan, offering an interactive experience to the user. We consider that the trade-off between interactivity and recommendation complexity exits, and that the second issue is preferable in this type of services. The details about the design and implementation of the system are described, along with the issues the system resolves and some guidelines for enhancement. 2011 Elsevier Ltd. All rights reserved.

Filtering for Improving the Geographic Information Search

This paper describes the GEOUJA System, a Geographical Information Retrieval (GIR) system submitted by the SINAI group of the University of Jaen in GeoCLEF 2007. The objective of our system is to filter the documents retrieved from an information retrieval (IR) subsystem, given a multilingual statement describing a spatial user need. The results of the experiments show that the new heuristics and rules applied in the geo-relation validator module improve the general precision of our system. The increasing of the number of documents retrieved by the information retrieval subsystem also improves the final results.

Query expansion on medical image retrieval: MeSH vs. UMLS

In this paper we explain experiments in the medical information retrieval task (ImageCLEFmed). We experimented with query expansion and the amount of textual information obtained from the collection. For expansion, we carried out experiments using MeSH ontology and UMLS separately. With respect to textual collection, we produced three different collections, the first one with caption and title, the second one with caption, title and the text of the section where the image appears, and the third one with the full text article. Moreover, we experimented with textual and visual search, along with the combination of these two results. For image retrieval we used the results generated by the FIRE software. The best results were obtained using MeSH query expansion on shortest textual collection (only caption and title) merging with the FIRE results.

Using information gain to improve the ImageCLEF 2006 collection

This paper describes the SINAI teams participation in both the ad hoc task and the medical task. For the ad hoc task we use a new Machine Translation system which works with several translators and heuristics. For the medical task, we have processed the set of collections using Information Gain (IG) to identify the best tags that should be considered in the indexing process.

Applying NLP techniques for query reformulation to information retrieval with geographical references

Geographic Information Retrieval (GIR) is an active and growing research area that focuses on the retrieval of textual documents according to a geographical criteria of relevance. However, since a GIR system can be treated as a traditional Information Retrieval (IR) system, it is important to pay attention to finding effective methods for query reformulation. In this way, the search results will improve their quality and recall. In this paper, we propose different Natural Language Processing (NLP) techniques of query reformulation related to the modification and/or expansion of both parts thematic and geospatial that are usually recognized in a geographical query. We have evaluated each of the reformulations proposed using GeoCLEF as an evaluation framework for GIR systems. The results obtained show that all proposed query reformulations retrieved relevant documents that were not retrieved using the original query.

SINAI at CLEF Ad-Hoc Robust Track 2007: Applying Google Search Engine for Robust Cross-Lingual Retrieval

We report our web-based query generation experiments for English and French collections in the Robust task of the CLEF Ad-Hoc track. We continued with the approach adopted in the previous year, although the model has been modified. Last year we used Google to expand the original query. This year we create a new expanded query in addition to the original one. Thus, we retrieve two lists of relevant documents, one for each query (the original and the expanded one). In order to integrate the two lists of documents, we apply a logistic regression merging solution. The results obtained are discouraging but the failure analysis shows that very difficult queries are improved by using both queries instead of the original query. The problem is to decide when a query is very difficult.We report our web-based query generation experiments for English and French collections in the Robust task of the CLEF Ad-Hoc track. We continued with the approach adopted in the previous year, although the model has been modified. Last year we used Google to expand the original query. This year we create a new expanded query in addition to the original one. Thus, we retrieve two lists of relevant documents, one for each query (the original and the expanded one). In order to integrate the two lists of documents, we apply a logistic regression merging solution. The results obtained are discouraging but the failure analysis shows that very difficult queries are improved by using both queries instead of the original query. The problem is to decide when a query is very difficult.

GEOUJA system the first participation of the University of Jaén at GeoCLEF 2006

This paper describes the first participation of the SINAI group of the University of Jaen in GeoCLEF 2006. We have developed a system made up of three main modules: the Translation Subsystem, that works with queries into Spanish and German against English collection; the Query Expansion subsystem, that integrates a Named Entity Recognizer, a thesaurus expansion module and a geographical information-gazetteer module; and the Information Retrieval subsystem. We have participated in the monolingual and the bilingual tasks. The results obtained shown that the use of geographical and thesaurus information for query expansion does not improve the retrieval in our experiments.