Arianna Pavone

Simone Faro, Arianna Pavone

The abelian pattern matching problem consists in finding all substrings of a text which are permutations of a given pattern. This problem finds application in many areas and can be solved in linear time by a naive sliding window approach. In this short communication we present a new class of algorithms based on a new efficient fingerprint computation approach, called Heap-Counting, which turns out to be fast, flexible and easy to be implemented. It can be proved that our solutions have a linear worst case time complexity and, in addition, we present an extensive experimental evaluation which shows that our newly presented algorithms are among the most efficient and flexible solutions in practice for the abelian matching problem in strings.

Domenico Cantone, Simone Faro, Arianna Pavone

String matching is the problem of finding all the substrings of a text which match a given pattern. It is one of the most investigated problems in computer science, mainly due to its very diverse applications in several fields. Recently, much research in the string matching field has focused on the efficiency and flexibility of the searching procedure and quite effective techniques have been proposed for speeding up the existing solutions. In this context, algorithms based on factors recognition are among the best solutions. In this paper, we present a simple and very efficient algorithm for string matching based on a weak factor recognition and hashing. Our algorithm has a quadratic worst-case running time. However, despite its quadratic complexity, experimental results show that our algorithm obtains in most cases the best running times when compared, under various conditions, against the most effective algorithms present in literature. In the case of small alphabets and long patterns, the gain in running times reaches 28%. This makes our proposed algorithm one of the most flexible solutions in practical cases.

Valeria Borzì, Simone Faro, Arianna Pavone et al.

In this paper we present SABRINA (Sentiment Analysis: a Broad Resource for Italian Natural language Applications) a manually annotated prior polarity lexical resource for Italian natural language applications in the field of opinion mining and sentiment induction. The resource consists in two different sets, an Italian dictionary of more than 277.000 words tagged with their prior polarity value, and a set of polarity modifiers, containing more than 200 words, which can be used in combination with non neutral terms of the dictionary in order to induce the sentiment of Italian compound terms. To the best of our knowledge this is the first prior polarity manually annotated resource which has been developed for the Italian natural language.