Semantic Roles and Argument Structure › Summary
Michael BrenndoerferMay 10, 20252 min read
Language AI HandbookAll chapters Semantic Roles and Argument Structure › SummarySection view · Sections in this chapter7 / 8 ↓
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- A predicate denotes a property or relation. Arguments realize selected participant slots, which lexical entries or constructions can license. Adjuncts add event information. Argumenthood judgments can be graded and depend on lexical and contextual information.
- Valency describes how many semantic participants a predicate sense selects and what kinds they are. Subcategorization frames describe the syntactic patterns available to realize arguments. The illustrated give sense permits two postverbal complement patterns, [NP NP] and [NP PP_to], with the giver as subject in both examples.
- Alternations expose differences between syntactic functions and participant roles. Passive and dative examples rearrange expressed participants; a causative/inchoative pair contrasts a named causer with a change whose cause is left open. Similar participant sets do not guarantee identical event meaning across all alternations.
- Thematic roles label participant properties and relations. Their definitions and granularity differ across inventories, and a single universal role-to-syntax mapping does not explain every lexical and constructional contrast.
- Dowty's proto-roles describe graded membership in two clusters of predicate entailments. Under the lexical-configuration conditions stated above, the Argument Selection Principle predicts subject and direct-object choice from the relative numbers of entailed proto-role properties. Natural forces and instruments can have Proto-Agent properties without being volitional.
- Frame semantics defines roles relative to a schematic scene. FrameNet frame elements and PropBank numbered arguments belong to different inventories; matching them requires the frame and predicate sense. The selected buy.01 and sell.01 roles illustrate why A0 is not a global semantic role name.
- Semantic role labeling identifies a predicate's arguments and assigns them roles. The demonstrated task is predicate-conditioned, encoded with BIO tags and relative-position features, and evaluated with exact-span precision, recall, and F1.
- Representative SRL approaches use parse features, neural tagging, joint span prediction, pretrained encoders, and text-to-text serialization. Compare their performance only under stated datasets, supplied labels, and scoring conventions.
- Practical limitations include inventory design, implicit arguments, nominal predicates, multilingual coverage, annotation disagreement, non-comparable evaluation conventions, and contamination. For downstream extraction, preserve the predicate sense, participant roles, and context such as polarity rather than treating a matching role set as a complete asserted fact.
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