Apply two of the our best models for NE's context (N = 5-7 words from the left + 5-7 words from the right);
Each best model for each student :) One model should be a simple, another model - a neural;
Note: try to design an architecture prototype of our annotator!
Hints: main classes (Annotator(Model model), Model (abstract class)), Handler
Naive architecture:
Application() (main point into our program, Annotator will be called here),
Annotator(Model), main method annotate(Dataset) --> NERConcordance, т.е. на входе - размеченный датасет Dataset, на выходе - таблица с результатами NERConcordance),
Model(наша сентимент-модель),
Dataset(размеченный датасет с BIO-аннотацией, список последовательностей Text),
Text(текст с разметкой BIO для каждого токена, т.е. список размеченных токенов),
Note: try to design an architecture prototype of our annotator!
Hints: main classes (Annotator(Model model), Model (abstract class)), Handler
Naive architecture: