To do this, it detects the arguments associated with the predicate or verb of a sentence and how they are classified into their specific roles.
The agent is "Mary," the predicate is "sold" (or rather, "to sell,") the theme is "the book," and the recipient is "John."
Another example is how "the book belongs to me" would need two labels such as "possessed" and "possessor" and "the book was sold to John" would need two other labels such as theme and recipient, despite these two clauses being similar to "subject" and "object" functions.
[2] His proposal led to the FrameNet project which produced the first major computational lexicon that systematically described many predicates and their corresponding roles.
[5] A better understanding of semantic role labeling could lead to advancements in question answering, information extraction, automatic text summarization, text data mining, and speech recognition.