An official website of the United States government
Abstract The launch of the FrameNet project in 1997 was both a crystallisation point of decades worth of theoretical investigations into lexical meaning by Charles J. Fillmore and colleagues, as well as the seed of an ongoing line of corpus-based and computational research that seeks to implement Fillmore’s theory of Frame Semantics in a way that both provides an interesting model relevant for further theorising and also is applicable practically for semantic analysis, lexicology, and lexicography. At the occasion of FrameNet’s 25th birthday, we want to introduce the project to a new generation of researchers but also take stock of, and report on, what has been achieved. We revisit the origins of the FrameNet project, assess its development and various changes in the years since it was first reported on in a special issue in the pages of this journal twenty years ago.
more »
« less
This content will become publicly available on May 23, 2026
FrameNet at 25: Results and Applications
Abstract This paper, a follow-up to Boas/Ruppenhofer/Baker (2024), reports on the results and applications of the FrameNet database. It spells out how FrameNet data have been used in linguistic theory, computational linguistics, multilingual lexicography, and foreign language teaching and learning. The paper also provides more information about the organization of the FrameNet project, inlcuding organizational, financial, and personal challenges.
more »
« less
- Award ID(s):
- 2335702
- PAR ID:
- 10615237
- Editor(s):
- Lew, Robert
- Publisher / Repository:
- European Association for Lexicography
- Date Published:
- Journal Name:
- International Journal of Lexicography
- Volume:
- 38
- Issue:
- 2
- ISSN:
- 0950-3846
- Page Range / eLocation ID:
- 159 to 189
- Subject(s) / Keyword(s):
- Frame Semantics lexicography computational semantics lexical semantics NLU
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We propose a means of augmenting FrameNet parsers with a formal logic parser to obtain rich semantic representations of events. These schematic representations of the frame events, which we call Episodic Logic (EL) schemas, abstract constants to variables, preserving their types and relationships to other individuals in the same text. Due to the temporal semantics of the chosen logical formalism, all identified schemas in a text are also assigned temporally bound "episodes" and related to one another in time. The semantic role information from the FrameNet frames is also incorporated into the schema's type constraints. We describe an implementation of this method using a neural FrameNet parser, and discuss the approach's possible applications to question answering and open-domain event schema learning.more » « less
-
We present NESL (the Neuro-Episodic Schema Learner), an event schema learning system that combines large language models, FrameNet parsing, a powerful logical representation of language, and a set of simple behavioral schemas meant to bootstrap the learning process. In lieu of a pre-made corpus of stories, our dataset is a continuous feed of “situation samples” from a pre-trained language model, which are then parsed into FrameNet frames, mapped into simple behavioral schemas, and combined and generalized into complex, hierarchical schemas for a variety of everyday scenarios. We show that careful sampling from the language model can help emphasize stereotypical properties of situations and de-emphasize irrelevant details, and that the resulting schemas specify situations more comprehensively than those learned by other systems.more » « less
-
null (Ed.)Abstract We introduce a novel paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrasing and discriminative span alignment. Our approach allows for the large-scale expansion of existing datasets or the rapid creation of new datasets using a small, manually produced seed corpus. We demonstrate our approach with experiments on the Berkeley FrameNet Project, a large-scale language understanding effort spanning more than two decades of human labor. With four days of training data collection for a span alignment model and one day of parallel compute, we automatically generate and release to the community 495,300 unique (Frame,Trigger) pairs in diverse sentential contexts, a roughly 50-fold expansion atop FrameNet v1.7. The resulting dataset is intrinsically and extrinsically evaluated in detail, showing positive results on a downstream task.more » « less
-
Modern knowledge bases have matured to the extent of being capable of complex reasoning at scale. Unfortunately, wide deployment of this technology is still hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and skilled knowledge engineers are in short supply. A way around this problem could be to acquire knowledge from text. However, the current knowledge acquisition technologies for information extraction are not up to the task because logic reasoning systems are extremely sensitive to errors in the acquired knowledge, and existing techniques lack the required accuracy by too large of a margin. Because of the enormous complexity of the problem, controlled natural languages (CNLs) were proposed in the past, but even they lack high enough accuracy. Instead of tackling the general problem of text understanding, our interest is in a related, but different, area of knowledge authoring—a technology designed to enable domain experts to manually create formalized knowledge using CNL. Our approach adopts and formalizes the FrameNet methodology for representing the meaning, enables incrementally-learnable and explainable semantic parsing, and harnesses rich knowledge graphs like BabelNet in the quest to obtain unique, disambiguated meaning of CNL sentences. Our experiments show that this approach is 95.6% accurate in standardizing the semantic relations extracted from CNL sentences—far superior to alternative systems.more » « less
