# Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments

arXiv: https://arxiv.org/abs/1704.02709 · HTML: https://mlsanity.com/paper/1704.02709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2017.

## Built on by (most cited first, 1 of 1)

- [Predicting Implicit Arguments in Procedural Video Instructions](https://mlsanity.com/paper/2505.21068/) (2025)

## Abstract (arXiv)

Implicit semantic role labeling (iSRL) is the task of predicting the semantic roles of a predicate that do not appear as explicit arguments, but rather regard common sense knowledge or are mentioned earlier in the discourse. We introduce an approach to iSRL based on a predictive recurrent neural semantic frame model (PRNSFM) that uses a large unannotated corpus to learn the probability of a sequence of semantic arguments given a predicate. We leverage the sequence probabilities predicted by the PRNSFM to estimate selectional preferences for predicates and their arguments. On the NomBank iSRL test set, our approach improves state-of-the-art performance on implicit semantic role labeling with less reliance than prior work on manually constructed language resources.