Bertol Arrieta

ZeuScansion: A tool for scansion of English poetry

We present a finite state technology 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 unknown word stress pattern guessing. The scanner also classifies its input according to the predominant type of metrical foot found. We also present a brief evaluation of the system using a gold standard corpus of human-scanned verse, on which a per-syllable accuracy of 86.78% is reached. The program uses open-source components and is released under the GNU GPL license.

An Assistant Tool for Verse-Making in Basque Based on Two-Level Morphology

In this paper we present a specialized word generator, which has been designed as an assistant tool for Basque troubadours. Such a tool allows verse-writers to generate all the words that match with a given word termination. We deal with some interesting aspects, i.e. the dimension of the generated list and the need to establish an order of relevance among the listed items. This work can be seen as a way of reusing computational linguistic tools in the context of the Basque cultural means of expression. The technical foundations of this tool lie in a two-level morphological processor. The way in which words must be generated (starting from the end of the word) leads us to invert the generation process.

Using Machine Learning Techniques to Build a Comma Checker for Basque

In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker for Basque. After several experiments, and trained with a little corpus of 100,000 words, the system guesses correctly not placing commas with a precision of 96% and a recall of 98%. It also gets a precision of 70% and a recall of 49% in the task of placing commas. Finally, we have shown that these results can be improved using a bigger and a more homogeneous corpus to train, that is, a bigger corpus written by one unique author.