semantic role labeling

**Semantic Role Labeling (SRL)**, or shallow semantic parsing, is the **task of identifying the predicate (action) in a sentence and classifying its arguments into semantic roles (Agent, Patient, Instrument, Locative)** — answering "Who did what to whom, where, when, and how?". **PropBank Roles** - **Arg0 (Proto-Agent)**: The doer/cause (Subject). - **Arg1 (Proto-Patient)**: The thing affected (Object). - **Arg2-Arg5**: Verb-specific roles (Beneficiary, Start Point, End Point). - **Adjuncts**: Time (AM-TMP), Location (AM-LOC), Manner (AM-MNR). **Example** - "John (Arg0) broke the window (Arg1) with a rock (Arg3) yesterday (AM-TMP)." **Why It Matters** - **Abstraction**: "The window was broken by John" and "John broke the window" have different syntax but IDENTICAL Semantic Roles. - **QA**: Directly maps natural language questions to structured database queries. - **Event Extraction**: The core component of event extraction systems. **Semantic Role Labeling** is **normalizing meaning** — disregarding passive/active voice or word order to extract the core event structure.