A Data-Driven Approach for Semantic Role Labeling from Induced Grammar Structures in Language
This addresses the challenge of adapting semantic role labeling to noisy and new languages without human annotation, though it is incremental as it builds on existing methods like ADIOS.
The paper tackles the problem of semantic role labeling by developing an unsupervised data-driven approach that learns grammar structures and rules from context, achieving results comparable to state-of-the-art models that rely on human-annotated data.
Semantic roles play an important role in extracting knowledge from text. Current unsupervised approaches utilize features from grammar structures, to induce semantic roles. The dependence on these grammars, however, makes it difficult to adapt to noisy and new languages. In this paper we develop a data-driven approach to identifying semantic roles, the approach is entirely unsupervised up to the point where rules need to be learned to identify the position the semantic role occurs. Specifically we develop a modified-ADIOS algorithm based on ADIOS Solan et al. (2005) to learn grammar structures, and use these grammar structures to learn the rules for identifying the semantic roles based on the context in which the grammar structures appeared. The results obtained are comparable with the current state-of-art models that are inherently dependent on human annotated data.