By Jan Hajič (auth.), Hrafn Loftsson, Eiríkur Rögnvaldsson, Sigrún Helgadóttir (eds.)
This e-book constitutes the complaints of the seventh foreign convention on Advances in ordinary Language Processing held in Reykjavik, Iceland, in August 2010.
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Additional info for Advances in Natural Language Processing: 7th International Conference on NLP, IceTAL 2010, Reykjavik, Iceland, August 16-18, 2010
This paper is focused on the statistical analysis. We have used R free software environment10 for statistical computing and graphics of the learners’ results. 1 Item Analysis and Distractor Evaluation The analysis of item responses in a quantitative way provides descriptions of item characteristics and test score properties among others. There are two main theories to address the problem: Classical Test Theory (CTT) and Item Response Theory (IRT). Both statistical theories have been already used in the evaluation of the automatic generation of distractors , .
It has also been applied in educational applications  and in the evaluation of synonym test questions . Our system makes use of Infomap software . This software uses a variant of LSA to learn vectors representing the meanings of words in a vector-space known as WordSpace. In our case, it indexes the documents in the corpora it processes and performs word to word semantic similarity computations based on the resulting model. As a result, the system extracts the words that best match a query according to the model.
Training required a very long time to converge, before introduction of phrase-based features, about 90 hours were necessary to train with the whole in-domain dataset. After the introduction of this kind of features, traning time decreased to about 40 hours. During the experiments, a real risk of local minima was detected. Performance of both rule based (baseline) and CRFs based reranking systems were evaluated in terms of accuracy, F-measure, precision and recall. The Table 1 shows the baseline rule system performance: rules perform well on in-domain sentences, while on out-of-domain sentences, the performance dramatically drops by losing 13,18% on accuracy and 8,89% on F-measure.