Sentence examples for classify sentences from inspiring English sources

In this paper, we present a Machine Learning approach that aims to classify sentences according to the PIBOSO scheme.

Specifically, we used a machine-learning (ML) approach, where an algorithm learns to classify sentences from a corpus of human-analyzed data.

Table 2 shows the UMLS semantic identifiers used to classify sentences.

After all rules have been generated, we used those rules to classify sentences in the testing data.

In the past, researchers have used machine learning methods to classify sentences in unstructured abstracts according to the generic headings of Aim, Method, Results and Conclusion [ 41- 43].

We design and assess the performance of an automatic key sentence selector, which classifies sentences into four argumentative moves: PURPOSE, METHODS, RESULTS and CONCLUSION.

This classifies sentences as stably true, stably false, and unstable.

From this it would follow that the logic of idiomatic English quantifiers is much stronger than FO, and that no effective procedure exists for classifying sentences as analytical or nonanalytical, synonymous or nonsynonymous.[86] This would be methodologically a very important result, showing that syntactic methods are even in principle insufficient in linguistic theorizing.

Grammatical complexity is rated with a research instrument that classifies sentences according to 8 levels of complexity.

Teufel and Moens [ 24] has proposed a strategy for summarization by classifying sentences from scientific texts into seven rhetorical categories.

The classifier classified a sentence to either "section heading" or "section heading with text" or "text".