Sentence examples for representations features from inspiring English sources

Different feature types, including general linguistic, character, case pattern, contextual, and word representations features, were exploited for our runs.

Deep Learning algorithms are one promising avenue of research into the automated extraction of complex data representations (features) at high levels of abstraction.

Our main focus on this improved system was to explore the effectiveness of the cost parameter optimization and word representations feature for the CEM subtask.

WR features: word representation features generated by Brown clustering [24], random indexing [25] and skip-gram [26].We followed the same method as in [31] to generate unsupervised word representation features using these three methods.

The generally applicable word representation features were reported to boost system performance significantly for both chemical and biomedical NER.

We observed that the whitespace tokenizer performs better than the BANNER simple tokenizer for extraction of word representation features.

For word representation features, we train Brown clustering models [28] and Word Vector (WV) models [17] on a large PubMed and PMC document collection.

The word representation features extracted from the unlabeled data boosted performance by a 1.37% and a 1.14% F-measure for CEM and CDI subtasks, respectively.

Recently, the studies that apply the word representation features induced on the large text corpus have reported improvement over baseline systems in many Natural Language Processing tasks [16 18].

Furthermore, we extracted two different types of word representation features (clustering-based representation features and distributional representation features) and integrated them with the SSVMs-based clinical NER system.

Different techniques have been used to generate word representation features.