SemEval-2019 Task 2: Unsupervised Lexical Frame Induction

Abstract

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.

Towards Layered Events and Schema Representations in Long Documents

“…Pradhan et al, 2007) in the future to facilitate comparisons. To retain the wider applicability of our embeddings, while improving results, we decided to use an approach similar to the "Bottom-up plus Top-down Prototype" one taken by QasemiZadeh et al (2019). We train a BERT network to decide if a pair of lexical units in their contexts evoke the same frame.…”

Language (Re)modelling: Towards Embodied Language Understanding

“…In this setting, learning compiled knowledge is closely related to automated knowledge base construction (Winn et al, 2019) or frame induction from text (QasemiZadeh et al, 2019). Our proposed paradigm suggests enriching classic symbolic knowledge representations (Speer et al, 2017) to executable form (Tamari et al, 2020).…”

Semantic frame induction through the detection of communities of verbs and their arguments

“…In our experiments, we use the same dataset used in the context of SemEval 2009 Task 2: Unsupervised Lexical Semantic Frame Induction (QasemiZadeh et al 2019). This dataset consists of sentences extracted from the PTB (Marcus et al 1993) with verbs annotated with FrameNet (Baker et al 1998) frames and arguments annotated with frame slots and generic semantic roles using the VerbNet format (Palmer et al 2017).…”