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
Boas, Hans C; Ruppenhofer, Josef; Baker, Collin F
(, International Journal of Lexicography)
Lew, Robert
(Ed.)
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.
Culkin, Ryan; Hu, J. Edward; Stengel-Eskin, Elias; Qin, Guanghui; Durme, Benjamin Van
(, Transactions of the Association for Computational Linguistics)
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.
Lawley, Lane; Schubert, Lenhart
(, Proceedings of the Workshop on Dimensions of Meaning: Distributional and Curated Semantics (DistCurate 2022))
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.
Li, Wen; Marino, Austin; Yang, Haoran; Meng, Na; Li, Li; Cai, Haipeng
(, ACM Transactions on Software Engineering and Methodology)
For many years now, modern software is known to be developed in multiple languages (hence termed asmultilingualormulti-languagesoftware). Yet, to date, we still only have very limited knowledge about how multilingual software systems are constructed. For instance, it is not yet really clear how different languages are used, selected together, and why they have been so in multilingual software development. Given the fact that using multiple languages in a single software project has become a norm, understanding language use and selection (i.e.,language profile) as a basic element of themultilingual constructionin contemporary software engineering is an essential first step. In this article, we set out to fill this gap with a large-scale characterization study on language use and selection in open-source multilingual software. We start with presentingan updated overviewof language use in 7,113 GitHub projects spanning the 5 past years by characterizing overall statistics of language profiles, followed bya deeper lookinto the functionality relevance/justification of language selection in these projects through association rule mining. We proceed with an evolutionary characterization of 1,000 GitHub projects for each of the 10 past years to providea longitudinal viewof how language use and selection have changed over the years, as well as how the association between functionality and language selection has been evolving. Among many other findings, our study revealed a growing trend of using three to five languages in one multilingual software project and the noticeable stableness of top language selections. We found a non-trivial association between language selection and certain functionality domains, which was less stable than that with individual languages over time. In a historical context, we also have observed major shifts in these characteristics of multilingual systems both in contrast to earlier peer studies and along the evolutionary timeline. Our findings offer essential knowledge on the multilingual construction in modern software development. Based on our results, we also provide insights and actionable suggestions for both researchers and developers of multilingual systems.
Lawley, Lane; Schubert, Lenhart
(, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop)
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.
@article{osti_10521298,
place = {Country unknown/Code not available},
title = {FrameNet at 25},
url = {https://par.nsf.gov/biblio/10521298},
DOI = {10.1093/ijl/ecae009},
abstractNote = {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.},
journal = {International Journal of Lexicography},
volume = {37},
number = {3},
publisher = {Oxford University Press},
author = {Boas, Hans_C and Ruppenhofer, Josef and Baker, Collin},
}
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