Semi-supervised Learning for Word Sense Disambiguation This work was awarded the Third Place in the EST 2013 Contest (ISSN 1850-2946) at the 42nd JAIIO (Annals of 42nd JAIIO - Argentine Journals of Informatics - ISSN 1850-2776). The original article in Spanish, titled “Desambiguación de Palabras Polisémicas mediante Aprendizaje Semi-supervisado”, can be found here: http://42jaiio.sadio.org.ar/proceedings/simposios/Trabajos/EST/20.pdf.
Table 2: Convergence, and proportions of final residual sets of examples.
['[EMPTY]', 'Target manzana', 'Target naturaleza', 'Target movimiento', 'Target tierra', 'Target interés', 'Target vidaciudad']
In the first part, we apply our approach to each target dataset. Clearly, any factor which increases the number of rules in the decision list will have a positive impact regarding this convergence. For example, a possible one would be the Factor 1: to perform the initial labeling with the original algorithm leads to more rules for the first iteration. Another way would involve Factor 3: by implementing also the “one sense per discourse” criterion, each iteration possibly provides more labeled examples to the next iteration. In both cases, for a larger dataset, it may lead to less iterations, although each one would require longer since it would have to inspect more rules. Then, these factors, as assumed in our experimental setting, have a negative impact in the final residual set. Factor 2 is of positive impact regarding convergence, due to the fact that a larger number of collocations are able to capture more reliably some linguistic phenomena that escape from the simple co-occurrence setting.