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Constraint 11 concerns the use of a regular entity in place of another construct.
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PRMs enable the combination of regular entity-relationship modeling aspects with means to perform enterprise architecture analysis under uncertainty.
Most prior work focused on recognizing regular entities that consist of continuous word sequences and are not overlapped with each other.
In this paper, we propose a neural network model called Bi-LSTM-CRF that consists of bidirectional (Bi) long short-term memories (LSTMs) and conditional random fields (CRFs) to identify regular entities and the components of irregular entities.
2 Combines individual CRFs to identify SYSTEMATIC and TRIVIAL entities with dictionary matching to identify FAMILY and ABBREVIATION entities, and regular expression matching to identify FORMULA and IDENTIFIER entities.
Run Description 1 Combines a CRF to identify SYSTEMATIC entities with dictionary matching to identify TRIVIAL, FAMILY, and ABBREVIATION entities, and regular expression matching to identify FORMULA and IDENTIFIER entities.
Commercials and product placements are becoming a more regular (if deceptively placed) entity within the body of TV series.
4 Combines individual CRFs to identify SYSTEMATIC, TRIVIAL, FAMILY, ABBREVIATION, and FORMULA entities with an individual regular expression matching to identify IDENTIFIER entities.
We evaluate our models on two datasets for recognizing regular and irregular biomedical entities.
Our previous observations were made in cell lines, therfore we now applied both antibodies in a side-by-side immunohistochemical analysis to a variety of tumor entities and regular tissues.
CTC is not able to differentiate this entity from a regular polyp, necessitating optical colonoscopy whenever the defect exceeds 5 mm.
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