Semantic Roles and Argument Structure

Consider two sentences:

• "The chef carved the roast."
• "The roast was carved by the chef."

Ignoring case and punctuation, the passive adds only was and by to the active sentence's word multiset. The roast is the direct object in the active sentence and the subject in the passive. The pair can describe one carving event with the same participants playing the same parts: the chef does the carving, the roast gets carved. What changes is how the language packages that event into syntax.

The syntactic analyses supply useful evidence, including passive subjects and by-phrases, but grammatical relation labels are not semantic roles. A role analysis adds a predicate-centered representation: the chef is the carver and the roast is the carved participant. The active and passive examples can describe the same carving situation, even though the sentences have different subjects. Recovering this participant structure is the problem we will work through.

This chapter is about that middle layer of meaning, the layer where a word like carve specifies that there is a carver and an entity being carved, and where a grammar decides which of those participants becomes the subject. The expressions that realize a predicate’s participant slots are its arguments, and semantic roles describe the parts the participants play. The task of finding them automatically is semantic role labeling, usually shortened to SRL.

The middle layer matters because it sits between two extremes. At one extreme is raw lexical identity, the words themselves. At the other is full logical meaning, including quantification, tense, modality, and inference. Semantic roles describe a selected level of participant structure. In the carving example, role identity survives the active/passive change even though the subject changes. The abstraction depends on the inventory: a predicate-specific role number and a frame-specific role name need not remain identical across different lexical choices.

SRL adds relations that entity types alone do not express. In Named Entity Recognition we identified mentions and their types. Here we connect spans through the predicate that organizes the clause. These participant relations alone do not distinguish an asserted event from a negated or merely possible one.

Consider a passage that mentions a minister, a contract, and a company. Entity types alone do not tell us whether the minister signed the contract, cancelled it, or discussed it. Predicate-argument analysis distinguishes these descriptions. A downstream extractor must also preserve the difference between "signed", "did not sign", and "might sign": the same signer and contract roles can occur in all three. A downstream extractor needs that additional context before treating the description as an asserted event.

The chapter moves through four layers. First, predicates and the arguments they license, including the distinction between arguments and adjuncts. Second, thematic roles, the classical inventory and its problems, culminating in Dowty's proto-role theory. Third, frame semantics, where meaning is defined relative to a schematic scene rather than a fixed role list. Fourth, semantic role labeling as an engineering task. We build the dataset, features and evaluator step by step and fit a scikit-learn classifier.

Each layer answers a slightly different question, and it is worth keeping those questions separate as we go. The first layer asks which participant slots a predicate sense or construction licenses. The second asks what kind of participant each one is. The third asks which scene the word evokes and how that scene names its participants. The fourth implements a narrower task: predicting labeled spans for a supplied predicate in a small, hand-annotated corpus. Its output consists of role labels for overt spans. Frame identification, sense disambiguation, and unexpressed participants fall outside this toy task.