Keywords: scansion, English, poetry, out-of-vocabulary wordsWe present a finite-state technology (FST) based system capable of performing metrical scansion of verse written in English. Scansion is the traditional task of analyzing the lines of a poem, marking the stressed and non-stressed elements and dividing the line into metrical feet. The system's workflow is composed of several subtasks designed around finite-state machines that analyze verse by performing tokenization, part-of-speech tagging, stress placement, and stress-pattern prediction for unknown words. The scanner also classifies poems according to the predominant type of metrical foot found. We present a brief evaluation of the system using a gold standard corpus of humanscanned verse, on which a per-syllable accuracy of 86.78% is achieved.The program uses open-source components and is released under the GNU GPL license.
Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.
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