Sentence examples for evaluations of text from inspiring English sources

From the result of the critical challenge evaluations of text classification (4, 13, 14), it is difficult to find big improvement (e.g. over 10%) against supervised learning with bag-of-words features, although some domain specific methods, e.g. named entity features (15 17) were reported to achieve 1 2% improvement.

The Text REtrieval Conference (TREC), a yearly workshop hosted by the US government's National Institute of Standards and Technology, provides the infrastructure necessary for large-scale evaluation of text retrieval methodologies.

In this study, we provided a comprehensive evaluation of text independent closed set speaker identification in the presence of AWGN and NSN types with a G.712 type handset at 16 kHz to provide benchmark evaluations of three different databases.

These approaches require annotated textual data for training and evaluation of text mining systems.

The "out-of-the-box" evaluation of text de-identification systems provided us with compelling insight about the best methods for de-identification of VHA clinical documents.

Collection of documents annotated with semantic entities and relationships are crucial resources to support development and evaluation of text mining solutions for the biomedical domain.

The BioCreative initiative (Critical Assessment of Information Extraction systems in Biology) (17, 18) is a community-wide effort for the evaluation of text mining and information extraction systems applied to the biological domain.

In addition, it contains annotations for relations between the entities, which provide a more exhaustive context for the training and evaluation of text mining tools supporting the curation of genetic variant databases.

If annotations were saved on textual data that had been manually reviewed but deemed not curation relevant, this could serve as negative training data, crucial for the development and evaluation of text mining applications.

In general, formal evaluation of text-to-concept mapping methods is limited in the bioinformatics literature.

The primary strength of this evaluation of text-based support for smoking cessation is that it uses a valid and precise estimated treatment effect.