A propos

This thesis presents a methodology to solve certain classification problems, particularly those involving sequential classification for Natural Language Processing tasks. It proposes the use of an iterative, error-based approach to improve classiffication performance, suggesting the incorporation of expert knowledge into the learning process through the use of knowledge rules.

We applied and evaluated the methodology to two tasks related with the detection  of hedging in scientific articles: those of hedge cue identification and hedge cue scope detection. Results are promising: for the first task, we improved baseline  results by 2.5 points in terms of F-score incorporating cue cooccurence information, while for scope detection, the incorporation of syntax information and rules  for syntax scope pruning allowed us to improve classification performance from  an F-score of 0.712 to a final number of 0.835.

Compared with state-of-the-art methods, results are competitive, suggesting  that the approach of improving classifiers based only on commited errors on a held  out corpus could be successfully used in other, similar tasks.

Additionaly, this thesis proposes a class schema for representing sentence analysis  in a unique structure, including the results of different linguistic analysis. This allows us to better manage the iterative process of classifier improvement, where  different attribute sets for learning are used in each iteration. We also propose to store attributes in a relational model, instead of the traditional text-based structures, to facilitate learning data analysis and manipulation.