FrameNet+: Fast Paraphrastic Tripling of FrameNet

By Ellie Pavlick, Travis Wolfe, Pushpendre Rastogi, Chris Callison-Burch, Mark Dredze, Benjamin Van Durme. 2015

61 citations recorded by the source corpus.

We increase the lexical coverage of FrameNet through automatic paraphrasing. We use crowdsourcing to manually filter out bad paraphrases in order to ensure a high-precision resource. Our expanded FrameNet contains an additional 22K lexical units, a 3-fold increase over the current FrameNet, and achieves 40% better coverage when evaluated in a practical setting on New York Times data.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Efficient Online Scalar Annotation with Bounded Support (2018, background): “…comparison, we assess it by comparison such as “burrito” is less frequent ( ≺ ) than “dog”. similarity, e.g., whether one word can be substituted for another in context (Pavlick et al., 2015), or whether an entire sentence is more or less similar than another (Marelli et al., 2014), and so on.”
Learning from others' mistakes: Avoiding dataset biases without modeling them (2020, methodology): “We evaluate on SciTail (Khot et al., 2018), GLUE benchmark’s diagnostic test (Wang et al., 2018), AddOneRTE (AddOne) Pavlick & Callison-Burch (2016), Deﬁ-nite Pronoun Resolution (DPR) (Rahman & Ng, 2012), FrameNet+ (FN+) (Pavlick et al., 2015) and Semantic Proto-Roles (SPR) (Reisinger et al., 2015).”
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (2018, methodology): “For example, Poliak et al. (2018a,b) previously noted the association between ungrammaticality and NOT-ENTAILED examples based on how White et al. (2017) recast the FrameNet+ dataset (Pavlick et al., 2015).”
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference (2018, methodology): “automatically converted, from existing semantic annotations: FrameNet Plus (FN+) (Pavlick et al., 2015), Definite Pronoun Resolution (DPR) (Rahman and Ng, 2012), and Semantic Proto-Roles (SPR) (Reisinger et al.”
A Survey of the Usages of Deep Learning for Natural Language Processing (2020, background): “Semantics Initiative [150] (FrameNet Plus or FN+ [151],”
Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering (2019, background): “There have been efforts to augment such resources with automatic methods (Snow et al., 2006; Pavlick et al., 2015b), but they are still confined to lexical level and sometimes require the use of other paraphrastic resources (Pavlick et al., 2015b).”
Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework (2017, methodology): “…(Palmer et al., 2005), (ii) the FrameNet Plus (FN+) dataset, which contains likelihood judgments about the paraphrase validity of frame triggers (Pavlick et al., 2015), and (iii) the Deﬁnite Pronoun Resolution (DPR) dataset, which contains annotations relevant to complex anaphora resolution…”