The Berkeley FrameNet Project

By Collin F. Baker, Charles J. Fillmore, John B. Lowe. 1998

FrameNet is a three-year NSF-supported project in corpus-based computational lexicography, now in its second year (NSF IRI-9618838, "Tools for Lexicon Building"). The project's key features are (a) a commitment to corpus evidence for semantic and syntactic generalizations, and (b) the representation of the valences of its target words (mostly nouns, adjectives, and verbs) in which the semantic portion makes use of frame semantics. The resulting database will contain (a) descriptions of the semantic frames underlying the meanings of the words described, and (b) the valence representation (semantic and syntactic) of several thousand words and phrases, each accompanied by (c) a representative collection of annotated corpus attestations, which jointly exemplify the observed linkings between "frame elements" and their syntactic realizations (e.g. grammatical function, phrase type, and other syntactic traits). This report will present the project's goals and workflow, and information about the computational tools that have been adapted or created in-house for this work.

Subfields: Natural Language Processing, Computational Linguistics & Semantics

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Citations in the corpus

SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference (2018, background): “Making such inference necessitates a rich understanding about everyday physical situations, including object affordances (Gibson, 1979) and frame semantics (Baker et al., 1998).”
SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (2017, background): “Significant research effort has focused on STS over English sentence pairs.2 English STS is a 1i.a., news headlines, video and image descriptions, glosses from lexical resources including WordNet (Miller, 1995; Fellbaum, 1998), FrameNet (Baker et al., 1998), OntoNotes (Hovy et al., 2006), web discus”
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints (2017, methodology): “Dataset We evaluate on imSitu (Yatskar et al., 2016) where activity classes are drawn from verbs and roles in FrameNet (Baker et al., 1998) and noun categories are drawn from WordNet (Miller et al., 1990).”
Movie Description (2016, methodology): “They rely on the semantic parser SEMAFOR (Das et al. 2012) trained on the FrameNet database (Baker et al. 1998), however, they limit the recognition only to two frames.”
A dataset for Movie Description (2015, methodology): “They also rely on a semantic parser (SEMAFOR [15]) trained on FrameNet database [7], however they limit the recognition only to two frames.”
A Primer in BERTology: What We Know About How BERT Works (2020, background): “Koval-eva et al. (2019) identiﬁed two heads of base BERT, in which self-attention maps were closely aligned with annotations of core frame semantic relations (Baker et al., 1998).”
Revealing the Dark Secrets of BERT (2019, methodology): “Speciﬁcally, we focused on whether BERT captures FrameNet’s relations be-tween frame-evoking lexical units (predicates) and core frame elements (Baker et al., 1998), and whether the links between them produce higher attention weights in certain speciﬁc heads.”
TGIF: A New Dataset and Benchmark on Animated GIF Description (2016, methodology): “We compare two different databases to represent semantic roles: WordNet [22] and FrameNet [2], which we refer to as SMT-WordNet and SMT-FrameNet, respectively.”
A semantic matching energy function for learning with multi-relational data (2013, methodology): “Finally, our ambition towards open-text semantic parsing is related to previous work by Shi and Mihalcea (2004), who proposed a rule-based system for open-text semantic parsing using WordNet and FrameNet (Baker et al. 1998) and by Giuglea and Moschitti (2006), who proposed a model to connect WordNet”
Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks (2008, background): “Large scale annotation projects such as TreeBank (Marcus et al., 1993), PropBank (Palmer et al., 2005), TimeBank (Pustejovsky et al., 2003), FrameNet (Baker et al., 1998), SemCor (Miller et al., 1993), and others play an important role in natural language processing research, encouraging the develop”
Machine Comprehension with Syntax, Frames, and Semantics (2015, background): “Frame semantic parsing (Das et al., 2014) is the problem of extracting frame-speciﬁc predicate-argument structures from sentences, where the frames come from an inventory such as FrameNet (Baker et al., 1998).”
Moments in Time Dataset: One Million Videos for Event Understanding (2018, methodology): “the Corpus of Contemporary American English (COCA) [11]). We then clustered the verbs according to their conceptual structure and meaning using the features for each verb from Propbank [28], FrameNet [5] and OntoNotes [21]. The clusters are sorted according to the combined frequency of use of each v”
Ordinal Common-sense Inference (2016, background): “Mining Common Sense Building large collections of common-sense knowledge can be done manually via professionals (Hobbs and Navarretta, 1993), but at considerable cost in terms of time and expense (Miller, 1995; Lenat, 1995; Baker et al., 1998; Friedland et al., 2004).”
SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation (2016, background): “During this time, a diverse set of genres and data sources have been explored (i.a., news headlines, video and image descriptions, glosses from lexical resources including WordNet (Miller, 1995; Christiane Fellbaum, 1998), FrameNet (Baker et al., 1998), OntoNotes (Hovy et al., 2006), web discussion ”
Unsupervised Learning of Narrative Schemas and their Participants (2009, methodology): “Instead, modern work on understanding has focused on shal-lower representations like semantic roles , which express at least one aspect of the semantics of events and have proved amenable to supervised learning from corpora like PropBank (Palmer et al., 2005) and Framenet (Baker et al., 1998).”
The Proposition Bank: An Annotated Corpus of Semantic Roles (2005, neutral): “The PropBank project and the FrameNet project at the International Computer Science Institute (Baker, Fillmore, and Lowe 1998) share the goal of documenting the syntactic realization of arguments of the predicates of the general English lexicon by annotating a corpus with semantic roles.”