Sentence examples for high-level text from inspiring English sources

These effects are attributed to high-level text processing.

Additionally, they have recently been demonstrated to be able to learn high-level text concepts from character-level representations of text [1] in a manner similar to how they learn features from and can classify images.

They want teachers to lead "high-level, text-based discussions"; "focus on process, not just content"; "create assignments for real audiences and with real purpose"; "teach argument, not persuasion"; and "increase text complexity".

Learning high-level concepts from text, such as those found in many applications of text classification, is a difficult task due to the many challenges associated with text mining and classification.

More recently, high-level abstraction of text documents learned using a Deep Boltzmann Machine (DBM -based formulation calleDBM -basedlicated Soformulation (ORSM) [21] demonstrated promising results for the task of text docalled classificatiOvernd Replicated

Thus, our proposed character embedding can be adapted to any big data domain where high-level understanding of text is required, such as sentiment analysis, webpage ontology and topic classification.

Eight stakeholders can be identified from our Petri Net with reference to the identification of high-level objects from text (cf. 2.1), depicted by two pools (corresponding to two pages of the PNML file) and six lanes with their flow of tasks and mutual interactions.

They build stamina as they read high-level informational and argumentative text in the paper.

Convolutional Neural Networks have been shown to be effective for text mining tasks including feature extraction and classification, and recently have been used to enable a classifier to be trained from character-level text data due to their ability to automatically identify and extract high-level concepts and features from text.

Thus, researchers must determine and implement the best feature engineering approach for each text classification task; however, deep learning allows us to skip this step by extracting and learning high-level features automatically from low-level text representations.

After months of high-level diplomatic haggling, the B.S.A. text contains all the key elements that the U.S. demanded: immunity from Afghan law, rights to military bases, and permission to continue counterterrorism operations.