Liviu P. Dinu

An efficient approach for the rank aggregation problem

This paper presents some computational properties of the rank-distance, a measure of similarity between partial rankings. We show how this distance generalizes the Spearman footrule distance, preserving its good computational complexity: the rank-distance between two partial rankings can be computed in linear time, and the rank aggregation problem can be solved in polynomial time. Further, we present a generalization of the rank-distance to strings, which permits to solve the median string problem in polynomial time. This appears rather surprising to us given the fact that for other non-trivial string distances, such as edit-distance, this problem is NP-hard.

On the syllabic similarities of romance languages

In this paper we study the syllabic similarity between Romance languages via rank distance. The results confirm the linguistical theories, bringing a plus of quantification and rigor.

An Efficient Rank Based Approach for Closest String and Closest Substring

This paper aims to present a new genetic approach that uses rank distance for solving two known NP-hard problems, and to compare rank distance with other distance measures for strings. The two NP-hard problems we are trying to solve are closest string and closest substring. For each problem we build a genetic algorithm and we describe the genetic operations involved. Both genetic algorithms use a fitness function based on rank distance. We compare our algorithms with other genetic algorithms that use different distance measures, such as Hamming distance or Levenshtein distance, on real DNA sequences. Our experiments show that the genetic algorithms based on rank distance have the best results.

Temporal Text Ranking and Automatic Dating of Texts

This paper presents a novel approach to the task of temporal text classification combining text ranking and probability for the automatic dating of historical texts. The method was applied to three historical corpora: an English, a Portuguese and a Romanian corpus. It obtained performance ranging from 83% to 93% accuracy, using a fully automated approach with very basic features.

Automatic Detection of Cognates Using Orthographic Alignment

Words undergo various changes when entering new languages. Based on the assumption that these linguistic changes follow certain rules, we propose a method for automatically detecting pairs of cognates employing an orthographic alignment method which proved relevant for sequence alignment in computational biology. We use aligned subsequences as features for machine learning algorithms in order to infer rules for linguistic changes undergone by words when entering new languages and to discriminate between cognates and non-cognates. Given a list of known cognates, our approach does not require any other linguistic information. However, it can be customized to integrate historical information regarding language evolution.

The naive bayes classifier in opinion mining: in search of the best feature set

This paper focuses on how naive Bayes classifiers work in opinion mining applications. The first question asked is what are the feature sets to choose when training such a classifier in order to obtain the best results in the classification of objects (in this case, texts). The second question is whether combining the results of Naive Bayes classifiers trained on different feature sets has a positive effect on the final results. Two data bases consisting of negative and positive movie reviews were used when training and testing the classifiers for testing purposes.

A parallel approach to syllabification

In this paper we propose a parallel manner of syllabification introducing some parallel extensions of insertion grammars. We use this grammars in an application to Romanian language syllabification.

Local patch dissimilarity for images

This paper aims to introduce a new distance measure for images, called Local Patch Dissimilarity. This new distance measure is inspired from rank distance which is a distance measure for strings. The distance measure introduced in this paper is based on patches. There are many other patch-based techniques used in image processing. Patches contain contextual information and have advantages in terms of generalization. An algorithm that computes the Local Patch Dissimilarity between two images is presented in this work. Experiments show that the extension of rank distance to images has very good results in image classification, more precisely in handwritten digit recognition.

An Approach to Syllables via some Extensions of Marcus Contextual Grammars

We propose and study a model of the graphical syllable using Marcus contextual grammars. For this purpose we introduce two new variants of Marcus contextual grammars: total Marcus contextual grammar with total leftmost derivation, and total Marcus contextual grammar with total leftmost derivation constrained by maximal use of selectors.

Clustering based on rank distance with applications on DNA

This paper aims to present two clustering methods based on rank distance. The K-means algorithm represents each cluster by a single mean vector. The mean vector is computed with respect to a distance measure. A new K-means algorithm based on rank distance is described in this paper. Hierarchical clustering builds models based on distance connectivity. Our paper introduces a new hierarchical clustering technique that uses rank distance. Experiments using mitochondrial DNA sequences extracted from several mammals demonstrate the clustering performance and the utility of the two algorithms.