Using particle swarm optimization to accurately identify syntactic phrases in free text

The present article reviews the application of Particle Swarm Optimization (PSO) algorithms to optimize a phrasing model, which splits any text into linguistically-motivated phrases. In terms of its functionality, this phrasing model is equivalent to a shallow parser. The phrasing model combines attractive and repulsive forces between neighbouring words in a sentence to determine which segmentation points are required. The extrapolation of phrases in the specific application is aimed towards the automatic translation of unconstrained text from a source language to a target language via a phrase-based system, and thus the phrasing needs to be accurate and consistent to the training data. Experimental results indicate that PSO is effective in optimising the weights of the proposed parser system, using two different variants, namely sPSO and AdPSO. These variants result in statistically significant improvements over earlier phrasing results. An analysis of the experimental results leads to a proposed modification in the PSO algorithm, to prevent the swarm from stagnation, by improving the handling of the velocity component of particles. This modification results in more effective training sequences where the search for new solutions is extended in comparison to the basic PSO algorithm. As a consequence, further improvements are achieved in the accuracy of the phrasing module

An efficient mechanism for stemming and tagging: the case of Greek language

Abstract. In an era that, searching the WWW for information becomes a tedious task, it is obvious that mainly search engines and other data mining mechanisms need to be enhanced with characteristics such as NLP in order to better analyze and recognize user queries and fetch data. We present an efficient mechanism for stemming and tagging for the Greek language. Our system is constructed in such a way that can be easily adapted to any existing system and support it with recognition and analysis of Greek words. We examine the accuracy of the system and its ability to support peRSSonal a medium constructed for offering meta-portal news services to internet users. We present experimental evaluation of the system compared to already existing stemmers and taggers of the Greek language and we prove the higher efficiency and quality of results of our system

Grouping Handwritten Letter Strokes Using a Fuzzy Decision Tree

This paper presents an algorithm for grouping strokes. This method includes two stages. Firstly, a set of strokes is transformed into a set of hypotheses that a group of strokes matches the pattern. For this purpose, a method for comparing small groups of strokes is proposed. Then, the set of hypotheses is selected with the use of a decision tree to get a proposition of a word