In this paper, we present our approach to SemEval-2013 Task 9.2. It is a feature rich classification using LIBSVM for Drug-Drug Interactions detection in the BioMedical domain. The features are extracted considering morphosyntactic, lexical and semantic concepts. Tools like openDMAP and TEES are used to extract semantic concepts from the corpus. The best F-score that we got for DrugDrug Interac...

We present BudgetedSVM, an open-source C++ toolbox comprising highly-optimized implementations of recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines, Low-rank Linearization SVM, and Budgeted Stochastic Gradient Descent. BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, s...

This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We considered two approaches, classification and regression, to provide fine-grained sentiment scores for a set of tweets that are rich in sarcasm, irony and metaphor. To this end, we combined a variety of standard lexical and syntactic features with specific features for c...

A new algorithm for the computation of a piecewise linear function separating two finite point sets in n-dimensional space is developed and the algorithm is applied to solve supervised data classification problems. The algorithm computes hyperplanes incrementally and it finds as many hyperplanes as necessary to separate two sets with respect to some tolerance. An error function is formulated an...

Traditional features, which are extracted from each frame, can not reflect the dynamic characteristics of emotion speech signal accurately. To solve this problem, first, without dividing the emotion speech into frames, novel global utterance-level features are proposed with multi-scale optimal wavelet packet decomposition; then, considering the case of little training samples, a fusion strategy...

Many gene selection algorithms have been applied in gene expression data analysis successfully. To solve different developing environments of these toolkits, such as rankgene (Su et al., 2003), and mRMR (http://research.janelia.org/peng/proj/mrmr/index.htm), perform data analysis and make algorithm comparison more flexible, we have developed a software package LIBGS including: 1) Seven new gene...

We present the CIC’s approach to the Author Profiling (AP) task at PAN 2017. This year task consists of two subtasks: gender and language variety identification in English, Spanish, Portuguese, and Arabic. We use typed and untyped character n-grams, word n-grams, and non-textual features (domain names). We experimented with various feature representations (binary, raw frequency, normalized freq...

In this paper, we present our experiments in TRECVID 2008 about High-Level feature extraction task. This is the first year for our participation in TRECVID, our system adopts some popular approaches that other workgroups proposed before. We proposed 2 advanced low-level features NEW Gabor texture descriptor and the Compact-SIFT Codeword histogram. Our system applied well-known LIBSVM to train t...

The multiclass classification problem is considered and resolved through coding and regression. There are various coding schemes for transforming class labels into response scores. An equivalence notion of coding schemes is developed, and the regression approach is adopted for extracting a low-dimensional discriminant feature subspace. This feature subspace can be a linear subspace of the colum...

We consider (a subclass of) convex quadratic optimization problems and analyze decomposition algorithms that perform, at least approximately, steepest-descent exact line search. We show that these algorithms, when implemented properly, are within ǫ of optimality after O(log 1/ǫ) iterations for strictly convex cost functions, and after O(1/ǫ) iterations in the general case. Our analysis is gener...