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

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

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

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