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. As a novel attack on the perennially vexing questions of the theoretical status of thematic roles and the inventory of possible roles, this paper defends a strategy of basing accounts of roles on more unified domains of linguistic data than have been used in the past to motivate roles, addressing in particular the problem of ARGUMENT SELECTION (principles determining which roles are associated with which grammatical relations). It is concluded that the best theory for describing this domain is not a traditional system of discrete roles (Agent, Patient, Source, etc.) but a theory in which the only roles are two cluster-concepts called PROTO-AGENT and PROTO-PATIENT, each characterized by a set of verbal entailments: an argument of a verb may bear either of the two proto-roles (or both) to varying degrees, according to the number of entailments of each kind the verb gives it. Both fine-grained and coarse-grained classes of verbal arguments (corresponding to traditional thematic roles and other classes as well) follow automatically, as do desired 'role hierarchies'. By examining occurrences of the 'same' verb with different argument configurations—e.g. two forms of psych predicates and object-oblique alternations as in the familiar spray load class—it can also be argued that proto-roles act as defaults in the learning of lexical meanings. Are proto-role categories manifested elsewhere in language or as cognitive categories? If so, they might be a means of making grammar acquisition easier for the child, they might explain certain other typological and acquisitional observations, and they may lead to an account of contrasts between unaccusative and unergative intransitive verbs that does not rely on deriving unaccusatives from underlying direct objects. We address the problem of recognizing situations in images. Given an image, the task is to predict the most salient verb (action), and fill its semantic roles such as who is performing the action, what is the source and target of the action, etc. Different verbs have different roles (e.g. attacking has weapon), and each role can take on many possible values (nouns). We propose a model based on Graph Neural Networks that allows us to efficiently capture joint dependencies between roles using neural networks defined on a graph. Experiments with different graph connectivities show that our approach that propagates information between roles significantly outperforms existing work, as well as multiple baselines. We obtain roughly 3-5 improvement over previous work in predicting the full situation. We also provide a thorough qualitative analysis of our model and influence of different roles in the verbs. We propose an account of linking patterns that does away with intermediary mechanisms such as thematic or actor undergoer hierarchies. Instead, constraints on word classes, defined by both syntactic and semantic criteria, encode generalizations between semantic roles and syntactic arguments. We show that the generalizations a linking theory needs to capture can be modeled via the same mechanisms as other lexical generalizations, using conditions specified within the hierarchy of word classes. Each condition provides a partial specification of the mapping between semantic roles and syntactic arguments. We argue that this constraint-based, verb-class-based view of linking offers several empirical advantages: partial regularities and exceptions are easily accommodated, fine-grained semantic distinctions relevant to linking are countenanced, and cross-cutting similarities between semantic and syntactic verb classes are economically captured.