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 second consequence follows from the first one: if it is unable to explain why some frames of roles are valid while the others are not, the obvious conclusion is that roles are not primitive elements in semantic representation. They are not unanalyzable @cite_8 . Treating semantic roles as primitives that are unrelated to one another makes valid role combination purely accidental @cite_5 .
@cite_5 @cite_8
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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. In this paper we introduce the problem of Visual Semantic Role Labeling: given an image we want to detect people doing actions and localize the objects of interaction. Classical approaches to action recognition either study the task of action classification at the image or video clip level or at best produce a bounding box around the person doing the action. We believe such an output is inadequate and a complete understanding can only come when we are able to associate objects in the scene to the different semantic roles of the action. To enable progress towards this goal, we annotate a dataset of 16K people instances in 10K images with actions they are doing and associate objects in the scene with different semantic roles for each action. Finally, we provide a set of baseline algorithms for this task and analyze error modes providing directions for future work. 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 present a new method for semantic role labeling in which arguments and semantic roles are jointly embedded in a shared vector space for a given predicate. These embeddings belong to a neural network, whose output represents the potential functions of a graphical model designed for the SRL task. We consider both local and structured learning methods and obtain strong results on standard PropBank and FrameNet corpora with a straightforward product-of-experts model. We further show how the model can learn jointly from PropBank and FrameNet annotations to obtain additional improvements on the smaller FrameNet dataset.