Julio Villena-Román

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.

MIRACLE at ImageCLEFmed 2007: Merging Textual and Visual Strategies to Improve Medical Image Retrieval

This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Retrieval task of ImageCLEF 2007. For this campaign, our challenge was to research on different merging strategies, i.e. methods of combination of textual and visual retrieval techniques. We have focused on the idea of performing all possible combinations of well-known textual and visual techniques in order to find which ones offer the best results in terms of MAP and analyze if the combined results may improve the individual ones.

MIRACLE at ad-hoc CLEF 2005: merging and combining without using a single approach

This paper presents the 2005 Miracle’s team approach to the Ad-Hoc Information Retrieval tasks. The goal for the experiments this year was twofold: to continue testing the effect of combination approaches on information retrieval tasks, and improving our basic processing and indexing tools, adapting them to new languages with strange encoding schemes. The starting point was a set of basic components: stemming, transforming, filtering, proper nouns extraction, paragraph extraction, and pseudo-relevance feedback. Some of these basic components were used in different combinations and order of application for document indexing and for query processing. Second-order combinations were also tested, by averaging or selective combination of the documents retrieved by different approaches for a particular query. In the multilingual track, we concentrated our work on the merging process of the results of monolingual runs to get the overall multilingual result, relying on available translations. In both cross-lingual tracks, we have used available translation resources, and in some cases we have used a combination approach.

DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in Twitter

This paper describes our participation at SemEval2014 sentiment analysis task, in both contextual and message polarity classification. Our idea was to compare two different techniques for sentiment analysis. First, a machine learning classifier specifically built for the task using the provided training corpus. On the other hand, a lexicon-based approach using natural language processing techniques, developed for a generic sentiment analysis task with no adaptation to the provided training corpus. Results, though far from the best runs, prove that the generic model is more robust as it achieves a more balanced evaluation for message polarity along the different test sets.

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.

MIRACLE at VideoCLEF 2008: topic identification and keyframe extraction in dual language videos

This paper describes the participation of MIRACLE research consortium at the VideoCLEF track at CLEF 2008. We took part in both the main mandatory Classification task (classify videos of television episodes using speech transcripts and metadata) and the Keyframe Extraction task (select key-frames that represent individual episodes from a set of supplied keyframes). Our system for the first task is composed of two main blocks: the core system knowledge base and the set of operational elements that are needed to classify the speech transcripts of the topic episodes and generate the output in RSS format. For the second task, our approach is based on the assumption that the most representative fragment (shot) of each episode is the one with the lowest distance to the whole episode, considering a vector space model. In the classification task, our runs ranked 3rd (out of 6 participants) in terms of precision.

MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation

This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2007. Our areas of expertise do not include image analysis, thus we approach this task as a machine-learning problem, regardless of the domain. FIRE is used as a black-box algorithm to extract different groups of image features that are later used for training different classifiers based on kNN algorithm in order to predict the IRMA code. The main idea behind the definition of our experiments is to evaluate whether an axis-by-axis prediction is better than a prediction by pairs of axes or the complete code, or vice versa.

Effect of connective functions in interactive image retrieval

This paper presents the participation of the MIRACLE team at the ImageCLEF 2005 interactive search task, in which we compare the efficiency of AND monolingual queries (which have to be precise and use the exact vocabulary, which may be difficult in a specialised search task) versus relevanceguided OR bilingual queries (a fuzzier and noisier search but which doesn’t require precise vocabulary and exact translations). User preferences and strategies in the context of cross-lingual interactive image retrieval are also analysed.

MIRACLE Progress in Monolingual Information Retrieval at Ad-Hoc CLEF 2007

This paper presents the 2007 MIRACLEs team approach to the AdHoc Information Retrieval track. The main work carried out for this campaign has been around monolingual experiments, in the standard and in the robust tracks. The most important contributions have been the general introduction of automatic named-entities extraction and the use of wikipedia resources. For the 2007 campaign, runs were submitted for the following languages and tracks: a) Monolingual: Bulgarian, Hungarian, and Czech. b) Robust monolingual: French, English and Portuguese.

From MOOCs to SPOCs… and from SPOCs to Flipped Classroom

The concept of SPOCs (Small Private Online Courses) emerged as a way of describing the reuse of MOOCs (Massive Open Online Courses) for complementing traditional on-campus teaching. But SPOCs can also drive an entire methodological change to make a better use of face-to-face time between students and teachers in the classroom. This paper presents the redesign and evaluation of a first-year programming course in several engineering degrees, with over 400 students overall, through the reuse of MOOCs as SPOCs on campus, combined with a flipped classroom strategy aimed at promoting active learning. Results from a students’ self-reported questionnaire show a very positive acceptance of the SPOC, which includes both videos and complementary formative activities, and an increase of motivation through the combination of the SPOC and activities implemented in lectures to flip the classroom.