Sentence examples for identification of sentences from inspiring English sources

Although individuals with alexithymia showed lower accuracy and longer reaction times during prosody identification of sentences with mismatching prosody and semantics in that previous study, these differences did not reach significance.

Kim et al. (2010) compared Support Vector Machine (SVM) and Bayes classifiers for the identification of sentences containing database accession numbers.

This task requires both the identification of sentences

Our hypothesis that identification of sentences mentioning target genes and an effect would also identify information on the GCs worked very well, as illustrated by the excellent results obtained.

We also predict improved identification of sentence type in matching cue conditions in comparison to mismatching cue conditions.

Table 4 Accuracy of answer-key identification No. of sentences Sentences with trigram answer-key Sentences with bigram answer-key Sentence with unigram answer-key No. of question with correct answer-key Accuracy 122 12248 60 122 95.902.

However, the identification of Participant sentences achieved poorer performance.

After the identification of the informative sentences the questions are generated by omitting the answer-keys which are selected by identifying domain specific words in the sentences.

Polajnar et al. (2011) addressed the identification of MEDLINE abstract sentences denoting protein protein interaction as a binary classification problem.

There are two subtasks: A) identification of GO evidence sentences (GOESs) for relevant genes in full-text articles and B) prediction of GO terms for relevant genes in full-text articles.

This article describes our participation of the Gene Ontology Curation task (GO task) in BioCreative IV where we participated in both subtasks: A) identification of GO evidence sentences (GOESs) for relevant genes in full-text articles and B) prediction of GO terms for relevant genes in full-text articles.