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

Eugénio Ribeiro,Andreia Sofia Teixeira,Ricardo Ribeiro,David Martins De Matos

doi:10.1007/s41109-020-00312-z

Abstract

Resources such as FrameNet, which provide sets of semantic frame definitions and annotated textual data that maps into the evoked frames, are important for several NLP tasks. However, they are expensive to build and, consequently, are unavailable for many languages and domains. Thus, approaches able to induce semantic frames in an unsupervised manner are highly valuable. In this paper we approach that task from a network perspective as a community detection problem that targets the identification of groups of verb instances that evoke the same semantic frame and verb arguments that play the same semantic role. To do so, we apply a graph-clustering algorithm to a graph with contextualized representations of verb instances or arguments as nodes connected by edges if the distance between them is below a threshold that defines the granularity of the induced frames. By applying this approach to the benchmark dataset defined in the context of SemEval 2019, we outperformed all of the previous approaches to the task, achieving the current state-of-the-art performance.

Highlights

A word may have different senses depending on the context in which it appears
We only experimented with different community detection algorithms after identifying the approach for generating contextualized representations that leads to the highest performance when using Chinese Whispers
Results on test data Chinese Whispers outperformed the remaining community detection algorithms on the development data, for consistency with the experiments regarding the clustering of verb instances according to the semantic frame they evoke, we assessed the performance of the Louvain Method and the Label Propagation algorithm on the test data

Summary

Introduction

A word may have different senses depending on the context in which it appears. different words that appear in the same context are typically related in some manner. Using less abstract terms and partially relying on Minsky’s definition in the context of knowledge representation and artificial intelligence (Minsky 1974), a semantic frame is a conceptual structure that describes a situation or entity, as well as its participants or properties. These participants are typically associated with the roles that they play in the context of the frame. We can identify three participants – Mary, John, and a car – which fill the seller, buyer, and goods frame slots and play the agent, recipient, and theme semantic roles, respectively

Methods

Results

Conclusion