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Given the importance of exploring relations for a more accurate understanding of language, the need for further advances in techniques for identifying and extracting them emerged, thus, the establishment of the Relation Extraction task was necessary.
The global optimization allows reliable automation of the extraction task.
Most systems use customized wrapper procedures to perform this extraction task.
More specifically, we formulate the relation extraction task as a classification task on chemical-disease pairs.
Fig. 5 Data extraction project instructions in CrowdFlower Fig. 6 Sample data extraction task in CrowdFlower.
This greatly increases the difficulties of the BM target feature extraction task.
Typically, the relation extraction task has been considered as a classification problem.
In this paper, we study on automatic single-document keyphrase extraction task.
The third observed issue refers to the advances obtained in the term extraction task.
The second issue is that the term extraction task may be used in different areas.
Fig. 7 Data extraction project instructions in PyBossa Fig. 8 Sample data extraction task in PyBossa.
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