By Hrafn Loftsson, Eirikur Rögnvaldsson, Sigrun Helgadottir
This ebook constitutes the complaints of the seventh overseas convention on Advances in typical Language Processing held in Reykjavik, Iceland, in August 2010.
Read or Download Advances in Natural Language Processing: 7th International Conference on NLP, IceTAL 2010, Reykjavik, Iceland, August 16-18, 2010, Proceedings PDF
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Extra resources for Advances in Natural Language Processing: 7th International Conference on NLP, IceTAL 2010, Reykjavik, Iceland, August 16-18, 2010, Proceedings
Due to the use of that statistical information, the larger the training set, the better the feature selection. Unfortunately, due to the high costs associated with data labeling, for many applications these datasets are very small. Because of this situation it is of great importance to search for alternative feature selection methods specially suited to deal with small training sets. In order to tackle the above problem, in this paper we propose to apply unsupervised extractive summarization as a feature selection technique; in other words, we propose reducing the set of features by representing documents by means of a representative subset of their sentences.
The marking was carried out manually. – Generation of distractors: for each stem and key selected in the previous step, distractors were generated. – Choosing the distractors: experts had to verify that the automatically generated distractors could not ﬁt the blank. 2 6 a. protection b. umbrella c. defense d. shadow. Automatic Distractor Generation for Domain Speciﬁc Texts 29 – Evaluation with learners: each learner read the MCQs embedded in a text and chose the correct answer among 4 options. – Item Analysis: based on learners’ responses, an item analysis process was carried out to measure the quality of the distractors.
The tagger application graphically shows, for each sentence, all the possible sequences of lexical categories and allows to select the best sequence or the best combination of sequences. Figure 2 shows a screenshot of the application. If the sequence selected does not include all the words in the sentence, the excluded words are labelled as Not Used. Sometimes the correct parse tree of a sentence is captured by a combination of two or more partial sequences. In order to prevent the bad tendency of the model to predict too many words as Not Used, words between two partial sequences are classiﬁed as Link.