Sentence examples for labeling sentences from inspiring English sources

In order to determine the upper bound of possible performance improvement by incorporating this feature, we tested the ideal case by manually labeling sentences as interaction or non-interaction.

This is done by extending a previous approach for labeling sentences in an abstract for general categories associated with scientific argumentation or rhetorical roles: Aim, Method, Results and Conclusion.

Otherwise, the annotator was instructed to label sentences as no.

Since some of our features rely on the rhetorical structure of the abstract, we have used AZPort to automatically label sentences according to their rhetorical category.

A human annotator was instructed to label sentences as yes whenever a logical connection between the sentence under analysis and its previous and/or its subsequent sentence was difficult to establish.

We also report the accuracy, defined as the percentage of correctly labeled sentences for each data set in each experiment.

Thus in this case Method labeled sentences are all the method related sentences excluding those that have been labeled as Intervention.

Finally we obtain a total of 20,895 sentences and we randomly choose only 1,000 sentences (probably 5% of all the sentences) for manual annotation; then we build two initial labeled data sets as initial training set Tinitial (500 labeled sentences) and test set (other 500 labeled sentences), respectively.

In addition, in order to make use of the information in the annotated sentences to improve the performance, after the ranking, we built a classifier for 12 high-level GO classes trained on labeled sentences to prune the result.

Ruch et al. [ 25] used Naive Bayes to label sentences into the four main argumentative moves, with the goal of finding an appropriate Conclusion sentence which appears to be the most informative [ 26], and therefore best candidate to enhance search results.

To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning.