Artificial neural network models use bi-directional LSTM which performs BIO tagging.

San Jose State University

- Sematic Role Labeling(SRL) is the process of finding the semantic role of each argument of each predicate in a sentence.

- Supervised machine learning approaches are used to achieve SRL systems.

- FrameNet and PropBank supplies the train and test datasets to develop SRL models.

Semantic Role Labeling

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

- The models that use  these algorithms starts by making a parse tree for each input sentence ,which is traversed to identify all the predicates.

- For each predicate the SRL iterates  through each node in the parse tree and perform supervised classification to determine whether the node serves as a semantic  role for the specified predicate and what type of role.

Feature-based algorithm for Semantic Role Labelling

 Neural Algorithm for Semantic Role Labeling

Precision, recall and  F1 score is used to evaluate whether each word constituent has been assigned the correct argument label.

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