M. T. Martín-Valdivia

Experiments with SVM to classify opinions in different domains

Recently, opinion mining is receiving more attention due to the abundance of forums, blogs, e-commerce web sites, news reports and additional web sources where people tend to express their opinions. Opinion mining is the task of identifying whether the opinion expressed in a document is positive or negative about a given topic. In this paper we explore this new research area applying Support Vector Machines (SVM) for testing different domains of data sets and using several weighting schemes. We have accomplished experiments with different features on three corpora. Two of them have already been used in several works. The last one has been built from Amazon.com specifically for this paper in order to prove the feasibility of the SVM for different domains.

Query expansion with a medical ontology to improve a multimodal information retrieval system

Searching biomedical information in a large collection of medical data is a complex task. The use of tools and biomedical resources could ease the retrieval of the information desired. In this paper, we use the medical ontology MeSH to improve a Multimodal Information Retrieval System by expanding the users query with medical terms. In order to accomplish our experiments, we have used the dataset provided by ImageCLEFmed task organizers for years 2005 and 2006. This dataset is composed of a multimodal collection (images and text) of clinical cases, a list of queries for each year, and a list of relevance judgments for each query to evaluate the results. The results from the experiments show that the use of a medical ontology to expand the queries greatly improves the results.

Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches

Sentiment polarity detection is one of the most popular tasks related to Opinion Mining. Many papers have been presented describing one of the two main approaches used to solve this problem. On the one hand, a supervised methodology uses machine learning algorithms when training data exist. On the other hand, an unsupervised method based on a semantic orientation is applied when linguistic resources are available. However, few studies combine the two approaches. In this paper we propose the use of meta-classifiers that combine supervised and unsupervised learning in order to develop a polarity classification system. We have used a Spanish corpus of film reviews along with its parallel corpus translated into English. Firstly, we generate two individual models using these two corpora and applying machine learning algorithms. Secondly, we integrate SentiWordNet into the English corpus, generating a new unsupervised model. Finally, the three systems are combined using a meta-classifier that allows us to apply several combination algorithms such as voting system or stacking. The results obtained outperform those obtained using the systems individually and show that this approach could be considered a good strategy for polarity classification when we work with parallel corpora.

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. n nWe 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. n nIn 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.

Using information gain to improve multi-modal information retrieval systems

Nowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval techniques (particularly images retrieval) have become an active research direction. In the past few years, a lot of content-based image retrieval (CBIR) systems have been developed. However, despite the progress achieved in the CBIR, the retrieval accuracy of current systems is still limited and often worse than only textual information retrieval systems. In this paper, we propose to combine content-based and text-based approaches to multi-modal retrieval in order to achieve better results and overcome the lacks of these techniques when they are taken separately. For this purpose, we use a medical collection that includes both images and non-structured text. We retrieve images from a CBIR system and textual information through a traditional information retrieval system. Then, we combine the results obtained from both systems in order to improve the final performance. Furthermore, we use the information gain (IG) measure to reduce and improve the textual information included in multi-modal information retrieval systems. We have carried out several experiments that combine this reduction technique with a visual and textual information merger. The results obtained are highly promising and show the profit obtained when textual information is managed to improve conventional multi-modal systems.

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.

Using Information Gain to Filter Information in CLEF CL-SR Track

This paper describes the first participation of the SINAI team in the CLEF 2007 CL-SR track. The SINAI team has only participated in the English task. The English collection includes segments of audio speech recognition and topics to evaluate the information retrieval systems. This collection contains interviews with survivors of the Holocaust manually segmented. Moreover, each segment includes different fields with extra information. The topics to evaluate the English task are available in Czech, English, French, German, Dutch and Spanish. This year, the team only wants to establish a first contact with the task and the collection. Thus, the collection has been pre-processed using the Information Gain technique in order to filter the fields with most relevant information. The Lemur toolkit has been the Information Retrieval system used in the experiments.

SINAI System: Combining IR Systems at ImageCLEFPhoto 2007

This paper describes the SINAI team participation in the ImageCLEFPhoto 2007 campaign. This year we have developed a system that combines the document lists retrieved by two Information Retrieval systems (Lemur and JIRS). Online machine translators have been used for the bilingual experiments. The results obtained show that if we only use title text our system works bad. Because of the low MAP values fusion method does not improve the results.

Improving Performance of Medical Images Retrieval by Combining Textual and Visual Information

This paper studies the combination of textual and visual information in a database of medical records in order to improve the performance of the multi-modal information retrieval system. The proposed model consists of two subsystems: a content-based information retrieval subsystem that performs the image retrieval and a textual information retrieval subsystem that performs the textual retrieval. The images and text are independently retrieved and then the partial resulting lists are mixed. A study of different weighting schemes has been accomplished and analyzed. The results obtained show that the proper integration of textual information improves conventional multi-modal systems.

Combining supervised and unsupervised polarity classification for non-english reviews

Two main approaches are used in order to detect the sentiment polarity from reviews. The supervised methods apply machine learning algorithms when training data are provided and the unsupervised methods are usually applied when linguistic resources are available and training data are not provided. Each one of them has its own advantages and disadvantages and for this reason we propose the use of meta-classifiers that combine both of them in order to classify the polarity of reviews. Firstly, the non-English corpus is translated to English with the aim of taking advantage of English linguistic resources. Then, it is generated two machine learning models over the two corpora (original and translated), and an unsupervised technique is only applied to the translated version. Finally, the three models are combined with a voting algorithm. Several experiments have been carried out using Spanish and Arabic corpora showing that the proposed combination approach achieves better results than those obtained by using the methods separately.