Catálogo de publicaciones - libros

In this paper we address the problem of matching musical renditions of the same piece of music also known as . We use an entropy based Audio-Fingerprint delivering a framed, small footprint AFP which reduces the problem to a string matching problem. The Entropy AFP has very low resolution (750 ms per symbol), making it suitable for flexible string matching.

We show experimental results using dynamic time warping (DTW), Levenshtein or distance and the Longest Common Subsequence (LCS) distance. We are able to correctly (100%) identify different renditions of masterpieces as well as pop music in less than a second per comparison.

The three approaches are 100% effective, but LCS and Levenshtein can be computed online, making them suitable for monitoring applications (unlike DTW), and since they are distances a metric index could be use to speed up the recognition process.

Topical web crawling technology is important for domain-specific resource discovery. Topical crawlers yield good recall as well as good precision by restricting themselves to a specific domain from web pages. There is an intuition that the text surrounding a link or the link-context on the HMTL page is a good summary of the target page. Motivated by that, This paper investigates some alternative methods and advocates that the link-context derived from reference page’s HTML tag tree can provide a wealth of illumination for steering crawler to stay on domain-specific topic. In order that crawler can acquire enough illumination from link-context, we initially look for some referring pages by traversing backward from seed URLs, and then build initial term-based feature set by parsing the link-contexts extracted from those reference web pages. Used to measure the similarity between the crawled pages’ link-context, the feature set can be adaptively trained by some link-contexts to relevant pages during crawling. This paper also presents some important metrics and an evaluation function for ranking URLs about pages relevance. A comprehensive experiment has been conducted, the result shows obviously that this approach outperforms Best-First and Breath-First algorithm both in harvest rate and efficiency.

We describe a music recommender model that uses intermediate agents to evaluate music composition according to their own rules respectively, and make recommendations to user. After user scoring recommended items, agents can adapt their selection rules to fit user tastes, even when user preferences undergo a rapid change. Depending on the number of users, the model can also be applied to such tasks as critiquing large numbers of music, image, or written compositions in a competitive contest with other judges. Several experiments are reported to test the model’s ability to adapt to rapidly changing conditions yet still make appropriate decisions and recommendations.

In this work, a data-driven approach for extracting answers from web-snippets is presented. Answers are identified by matching contextual distributional patterns of the expected answer type(EAT) and answer candidates. These distributional patterns are directly learnt from previously annotated tuples {}, and the learning mechanism is based on the principles language acquisition. Results shows that this linguistic motivated data-driven approach is encouraging.

Nowadays, there is an increasing interest in research on QA over restricted domains. Concretely, in this paper we will show the process of question analysis in a medical QA system. This system is able to obtain answers to different natural language questions according to a question taxonomy. In this system we combine the use of NLP techniques and biomedical resources. The main NLP technique is the use of logic forms and the pattern matching technique in this question analysis performance.

Automatic categorization has been shown to be an accurate alternative to manual categorization in which documents are processed and automatically assigned to pre-defined categories. The accuracy of different methods for categorization has been studied largely, but their efficiency has seldom been mentioned. Aiming to maintain effectiveness while improving efficiency, we proposed a fast algorithm for text categorization and a compressed document vector representation method based on a novel class space model. The experiments proved our methods have better efficiency and tolerable effectiveness.

How to improve the accuracy of categorization is a big challenge in text categorization. This paper proposes a high performance prototype system for Chinese text categorization, which mainly includes feature extraction subsystem, feature selection subsystem, and reliability evaluation subsystem for classification results. The proposed prototype system employs a two-step classifying strategy. First, the features that are effective for all testing texts are used to classify texts. Then, the reliability evaluation subsystem evaluates the classification results directly according to the outputs of the classifier, and divides them into two parts: texts classified reliable or not. Only for the texts classified unreliable at the first step, go to the second step. Second, a classifier uses the features that are more subtle and powerful for those texts classified unreliable to classify the texts. The proposed prototype system is successfully implemented in a case that exploits a Naive Bayesian classifier as the classifier in the first and second steps. Experiments show that the proposed prototype system achieves a high performance.

This article aims at presenting a methodological approach for classifying educational conference papers by employing a Bayesian Network (BN). A total of 400 conference papers were collected and categorized into 4 major topics (, C, , and ). In this study, we have implemented a 80-20 split of collected papers. 80% of the papers were meant for keywords extraction and BN parameter learning whereas the other 20% were aimed for predictive accuracy performance. A feature selection algorithm was applied to automatically extract keywords for each topic. The extracted keywords were then used for constructing BN. The prior probabilities were subsequently learned using the Expectation Maximization (EM) algorithm. The network has gone through a series of validation by human experts and experimental evaluation to analyze its predictive accuracy. The result has demonstrated that the proposed BN has outperformed Naïve Bayesian Classifier, and BN learned from the training data.

This paper presents an application of an ontology based system for automated text analysis using a sample of a drilling report to demonstrate how the methodology works. The methodology used here consists basically of organizing the knowledge related to the drilling process by elaborating the ontology of some typical problems. The whole process was carried out with the assistance of a drilling expert, and by also using software to collect the knowledge from the texts. Finally, a sample of drilling reports was used to test the system, evaluating its performance on automated text classification.

This paper proposes a topic selection method for web documents using ontology hierarchy. The idea of this approach is to utilize the ontology structure in order to determine a topic in a web document. In this paper, we propose an approach for improving the performance of document clustering as we select the topic efficiently based on domain ontology. We preprocess the web documents for keywords extraction using formula and we build domain ontology as we branch off the partial hierarchy from WordNet using an automatic domain ontology building tool in preprocessing step. And we select a topic for the web documents based on domain ontology structure. Finally we realized that our approach contributes the efficient document clustering.