Semantic Roles and Argument Structure › Thematic Roles

Gruber (1965) and Fillmore (1968) analyzed semantic participant relations separately from surface grammatical relations such as subject and object.

The passive and dative shift examples keep the participant roles while changing how they are expressed. In both spray/load variants, the paint is the material and the wall is the receiving surface. The direct-object choice can change what the sentence implies about the extent of the effect. This motivates labeling participants independently of their grammatical positions. Thematic roles give us a vocabulary for who initiates an event, who is affected by it, and who receives something.

The following table fixes the thematic-role conventions used in this chapter. Role definitions vary across analyses, so the diagnostic column gives illustrative cues to examine rather than tests that determine a label by themselves.

Read the table as a set of prototypes. Use deliberately to probe whether a reading is intentional and ask "what happened to X?" to probe affectedness. Both cues depend on the reading and can leave the role undecided.

The table uses Agent for a volitional instigator and Cause for a nonvolitional instigator. On an intentional reading, the chef and the storm illustrate that contrast. Theme and Patient require more care: some inventories use Theme broadly for affected participants. Here, Patient covers affected or changed entities, while Theme covers moved, located, or described entities without requiring a change of state; it does not establish that the terms are universally disjoint.

These conventions help explain why a label's definition must accompany its name. A storm can cause destruction without intending it. A rolling ball changes location, while carving changes the roast in another way. Whether a resource calls either participant Theme, Patient, or a frame-specific name depends on the distinctions that resource chooses to encode. Cross-resource comparisons must align the definitions rather than compare strings alone.

Given a verb's participants, how does a language decide which one becomes the subject? A thematic hierarchy proposes a ranking to constrain that mapping. Dowty derives the following schematic ordering from proto-role entailments, with the qualification "usually" (1991, Section 8.2, example 36):

Agent > {Instrument, Experiencer} > Patient > {Source, Goal} (usually)

Here > means both "outranks for subject" and "outranks an oblique for direct object" under Dowty's argument-selection assumptions. The ranking favors an agent over an affected participant in a simple active transitive example. In "The key opened the door", the lexical construction licenses an instrument subject. That realization is compatible with the schematic ranking. Passive syntax must be analyzed separately from lexical subject selection.

The linking problem concerns mappings from participant semantics to grammatical relations. A hierarchy proposes regularities that can be reused across predicates, rather than separately listing every realization. Its predictions still depend on the role definitions, the construction, and the predicate classes to which it applies. It is an analysis to evaluate against examples, not an ordering that follows from the labels themselves.

The ranking leaves out the entailment analysis from which it was derived. Applying it therefore requires checking the participants' properties and the relevant construction. The psych-verb contrast makes the limits of label names alone concrete:

• "The student feared the exam." (Experiencer subject, Stimulus object)
• "The exam frightened the student." (Stimulus subject, Experiencer object)

The examples place Experiencer and Stimulus in different grammatical positions. A fixed ranking of those two labels alone would not distinguish the realizations. The lexical meanings and relevant event properties must also be analyzed; this is the contrast examined in Dowty's discussion of psych predicates.

These psych predicates also differ in lexical meaning. Fear describes an experiencer's state, whereas frighten can describe causing fear or a change in emotional state. The pair therefore does not prove that two synonymous predicates simply reverse a fixed role order. It shows why both participant properties and lexical event structure matter when analyzing subject selection; Dowty discusses these contrasts in his treatment of psych predicates (1991, Section 8.3).

This section examines five issues: role granularity, definitions, assignment choices, links to syntax, and annotation reliability.

Granularity. How many roles should there be? If Goal, Recipient, and Beneficiary are all separate, why not separate "Goal-of-transfer" from "Goal-of-motion"? If they are merged, distinctions that matter for downstream tasks disappear. Different resources choose different stopping points according to the distinctions they represent.

Inventory granularity determines which distinctions the annotations can express. Merging Recipient and Goal removes a distinction between transfer and other endpoints; separating them requires a definition for each. The appropriate choice depends on the task and annotation guidelines. Granularity alone does not determine annotation speed, agreement, or model accuracy, which must be measured for the particular resource and annotators.

Definition dependence. Theme does not have one fixed extension across theories. Dowty discusses differences between stative Theme, affected participants, and incremental themes (1991, Sections 2 and 8.1). Judging whether a model's Theme labels are semantically appropriate requires specifying their intended definition. This motivates documenting the inventory; it does not establish that one label scheme is inherently better for machine learning.

Assignment depends on the intended contribution. In "The gardener planted roses in the garden", the planting-site reading identifies where the roses end up. A resource may represent that selected site with Goal, Destination, or a frame-specific location role. A separate event-setting analysis asks where the event occurs. The fact that the garden can be both endpoint and setting does not, by itself, establish two labels for the same PP within one fixed inventory. An annotation project must specify the distinction and assess whether annotators follow it.

A selected endpoint and the setting of an event are distinct questions, even when a phrase appears to answer both. A project may choose one label, permit several, or use frame-specific definitions. The example alone does not establish how often disagreements occur or whether training and adjudication would resolve them.

Roles do not determine syntax, and syntax does not determine roles. The passive, the dative shift, the spray/load alternation, instrument subjects, and psych verbs all show that the mapping is many-to-many.

The many-to-many mapping raises a linking question. If roles determined syntax, we could predict grammatical relations from semantic labels. If syntax determined roles, we could predict semantic labels from grammatical relations. Neither shortcut follows from the labels alone. The mapping is mediated by the verb's lexical entry, the construction, and the discourse context, which is why SRL systems need predicate-specific information.

Annotation reliability. Disagreements may concern span boundaries, predicate senses, or role definitions. These are different sources of error and should be measured separately. Comparing agreement across SRL and part-of-speech projects requires matched protocols; a general ordering does not follow merely from one task seeming more abstract.

A model is evaluated against the released reference annotations, which may differ from another defensible analysis of the text. Annotation agreement helps characterize uncertainty in that reference, but a pairwise human agreement score is not a mathematical upper bound on model accuracy against one fixed gold set. Report annotation procedures and adjudication alongside the metric, and inspect disagreements before deciding whether a model prediction or a reference label needs correction.

Dowty (1991) proposes two cluster concepts, Proto-Agent and Proto-Patient, characterized by predicate entailments. Arguments can satisfy different numbers of their properties, share a proto-role, qualify partially for both, or have neither. The lists are preliminary and need not be exhaustive.

The comparison asks which agent-like and patient-like properties a predicate entails about each argument. Dowty allows unclear property boundaries and possible differences in the weight of entailments. Counting fixed binary properties, as in the later tutorial table, is a simplified way to illustrate the comparison; it is not a complete implementation of the theory.

The properties Dowty associates with the Agent proto-role are:

1. Volitional involvement in the event or state.
2. Sentience, and/or perception.
3. Causing an event or change of state in another participant.
4. Movement relative to the position of another participant.
5. Existence independently of the event named by the verb, which Dowty lists in parentheses.

The properties of the Patient proto-role are:

1. Undergoes a change of state.
2. Is an incremental theme, with relevant parts of the participant corresponding to parts of the event as it progresses.
3. Is causally affected by another participant.
4. Is stationary relative to the movement of another participant.
5. Does not exist independently of the event, or does not exist at all, also listed in parentheses.

Dowty explicitly qualifies the independent-existence properties: he is uncertain whether they belong to the proto-role definition or to discourse associations of subjecthood (1991, Section 7, properties 27e and 28e). He also asks whether relative stationarity should be omitted from the Patient list (property 28d, Section 9.3.3). The lists should therefore be read as a proposal with stated qualifications, not ten equally settled tests. He also qualifies movement: he treats movement as agent-like only when no other participant in the named event causes or interrupts it. For movement to count as patient-like change, he usually requires it to be described to or from a specified location. In these comparisons, causation takes priority over movement (pp. 573–574).

Dowty's Argument Selection Principle relates lexical subject selection to the greatest number of entailed Proto-Agent properties and direct-object selection to the greatest number of Proto-Patient properties, subject to the qualifications just discussed. In note 17, Dowty limits the apparent scope of the principle to two-place predicates with a grammatical subject and a true direct object. The first corollary permits alternative lexicalizations when arguments have approximately equal Proto-Agent and Proto-Patient entailments. The second compares nonsubject arguments of three-place predicates when selecting direct versus oblique objects (Dowty, 1991, p. 576).

The selection principle concerns lexical argument selection, with the relevant subject and direct-object configurations specified in the proposal. It is not a rule that generates every surface subject from a count of observed event details. Passive syntax and the behavior of psych predicates require their own analysis; the principle alone does not guarantee a unique surface arrangement.

This reframing allows nonvolitional participants to have agent-like properties. A key used to open a door can cause a change without being sentient, and a destructive storm can cause damage without intending it. Which properties are entailed must be checked for the predicate and interpretation, rather than filled in from a typical imagined scene. The later binary table gives selected illustrative judgments to demonstrate property-count arithmetic; its numbers are not evidence that a particular subject is uniquely required by the theory.

The proto-role proposal concerns argument selection, not the label strings an annotator must write. PropBank explicitly cites Proto-Agent and Proto-Patient tendencies when describing A0 and A1 (Palmer et al., 2005, Section 3). The tendencies do not define a universal algorithm that selects the most agent-like phrase as A0: the predicate sense's roleset supplies the annotation definitions. FrameNet instead defines frame elements relative to a frame.