Semantic role theory considers roles as a small universal set of unanalyzed entities. It means that formally there are no restrictions on role combinations. We argue that the semantic roles co-occur in verb representations. It means that there are hidden restrictions on role combinations. To demonstrate that a practical and evidence-based approach has been built on in-depth analysis of the largest verb database VerbNet. The consequences of this approach are considered.
The question about internal organization inside of the list of roles can be rephrased in a more general form: are there any types of relations across roles? Many linguists pointed out that roles are related in some way. They paid attention to the fact that some semantic roles co-occur while the others do not. For example, the role frame Agent, Patient, Instrument represents lots of verbs but the role frames Patient, Experiencer or Beneficiary, Goal do not represent any verb at all. In FrameNet @cite_14 there are 2 types of role-to-role relations -- "requires" and "excludes" -- that indicate for a specific role in a frame which roles are required and which are excluded.
@cite_14
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We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Given an input sentence and a target word and frame, the system labels constituents with either abstract semantic roles, such as AGENT or PATIENT, or more domain-specific semantic roles, such as SPEAKER, MESSAGE, and TOPIC.The system is based on statistical classifiers trained on roughly 50,000 sentences that were hand-annotated with semantic roles by the FrameNet semantic labeling project. We then parsed each training sentence into a syntactic tree and extracted various lexical and syntactic features, including the phrase type of each constituent, its grammatical function, and its position in the sentence. These features were combined with knowledge of the predicate verb, noun, or adjective, as well as information such as the prior probabilities of various combinations of semantic roles. We used various lexical clustering algorithms to generalize across possible fillers of roles. Test sentences were parsed, were annotated with these features, and were then passed through the classifiers.Our system achieves 82 accuracy in identifying the semantic role of presegmented constituents. At the more difficult task of simultaneously segmenting constituents and identifying their semantic role, the system achieved 65 precision and 61 recall.Our study also allowed us to compare the usefulness of different features and feature combination methods in the semantic role labeling task. We also explore the integration of role labeling with statistical syntactic parsing and attempt to generalize to predicates unseen in the training data.