FRAME SEMANTICS is a research program in empirical semantics which emphasizes the continuities between language and experience, and provides a framework for presenting the results of that research. A FRAME is any system of concepts related in such a way that to understand any one concept it is necessary to understand the entire system; introducing any one concept results in all of them becoming available. In Frame Semantics, a word represents a category of experience; part of the research endeavor is the uncovering of reasons a speech community has for creating the category represented by the word and including that reason in the description of the meaning of the word.Similar or comparable notions have developed and are employed in other fields, particularly artificial intelligence and cognitive psychology. (The near simultaneity and mutual recognition of the use of the concept among scholars in different fields are worth noting.) Perhaps of greatest influence for artificial intelligence was Minsky's work that presented frame as a cover term for "a data-structure representing a stereotyped situation" (1975:212). The work included a characterization of several types of frames, some of which corresponded to other uses by linguists (including Fillmore, 1968) and psychologists. Among the latter were Schank and Abelson (1975) in whose work on story understanding the term script (comparable to Minsky's frame) refers to knowledge structures for sequences of events, a well-known example being the restaurant script. (See Tannen 1979 for a more detailed overview of the concept as used by other researchers, including anthropologists and sociologists, and an exposition of frame as "structures of expectations " (1979:144). See also Fillmore 1985a for additional citations of later works employing the concept.)The frame notion used in Frame Semantics can be traced most directly to case frames (Fillmore, 1968). In case grammar, the semantic roles of the arguments of predicates were considered crucial to the characterization of verbs and clauses. Case frames were understood as "characterizing a small abstract 'scene' or 'situation', so that to understand the semantic structure of the verb it was necessary to understand the properties of such schematized scenes" (Fillmore 1982:115). In the early papers on Frame Semantics, a distinction is drawn between scene and frame, the former being a cognitive, conceptual, or experiential entity and the latter being a linguistic one (e.g. Fillmore, 1975). In later works, scene ceases to be used and a frame is a cognitive structuring device, parts of which are indexed by words associated with it and used in the service of understanding (e.g. Fillmore, 1985a). (See Petruck (forthcoming) for a fuller account of the development of the frame idea in lexical semantics.)The notion can be exemplifed with the Commercial Transaction Frame, whose elements include a buyer, a seller, goods, and money. (Note that these frame elements have been designated in terms of situational roles; this contrasts with t...

While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization. This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL). According to ECL, natural language is inherently executable (like programming languages), driven by mental simulation and metaphoric mappings over hierarchical compositions of structures and schemata learned through embodied interaction. This position paper argues that the use of grounding by metaphoric inference and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.

This paper presents Unsupervised Lexical Frame Induction, Task 2 of the International Workshop on Semantic Evaluation in 2019. Given a set of prespecified syntactic forms in context, the task requires that verbs and their arguments be clustered to resemble semantic frame structures. Results are useful in identifying polysemous words, i.e., those whose frame structures are not easily distinguished, as well as discerning semantic relations of the arguments. Evaluation of unsupervised frame induction methods fell into two tracks: Task A) Verb Clustering based on FrameNet 1.7; and B) Argument Clustering, with B.1) based on FrameNet's core frame elements, and B.2) on VerbNet 3.2 semantic roles. The shared task attracted nine teams, of whom three reported promising results. This paper describes the task and its data, reports on methods and resources that these systems used, and offers a comparison to human annotation.

Abstract. This paper describes FrameNet [9,1,3], an online lexical resource for English based on the principles of frame semantics [5,7,2]. We provide a data category specification for frame semantics and FrameNet annotations in an RDF-based language. More specifically, we provide an RDF markup for lexical units, defined as a relation between a lemma and a semantic frame, and frame-to-frame relations, namely Inheritance and Subframes. The paper includes simple examples of FrameNet annotated sentences in an XML/RDF format that references the project-specific data category specification. Frame Semantics and the FrameNet ProjectFrameNet's goal is to provide, for a significant portion of the vocabulary of contemporary English, a body of semantically and syntactically annotated sentences from which reliable information can be reported on the valences or combinatorial possibilities of each item included. A semantic frame is a script-like structure of inferences, which are linked to the meanings of linguistic units (lexical items). Each frame identifies a set of frame elements (FEs), which are frame-specific semantic roles (participants, props, phases of a state of affairs). Our description of each lexical item identifies the frames which underlie a given meaning and the ways in which the FEs are realized in structures headed by the word. The FrameNet database documents the range of semantic and syntactic combinatory possibilities (valences) of each word in each of its senses, through manual annotation of example sentences and automatic summarization of the resulting annotations. FrameNet I focused on governors, meaning that for the most part, annotation was done in respect to verbs; in FrameNet II, we have been annotating in respect to governed words as well. The FrameNet database is available in XML, and can be displayed and queried via the web and other interfaces. FrameNet data has also been translated into the DAML+OIL extension to XML and the Resource Description Framework (RDF). This paper will explain the theory behind FrameNet, briefly discuss the annotation process, and then describe how the FrameNet data can be represented in RDF, using DAML+OIL, so that researchers on the semantic web can use the data.

This paper attempts to bridge the gap between FrameNet frames and inference. We describe a computational formalism that captures structural relationships among participants in a dynamic scenario. This representation is used to describe the internal structure of FrameNet frames in terms of parameters for event simulations. We apply our formalism to the commerce domain and show how it provides a flexible means of accounting for linguistic perspective and other inferential effects.