Semantic Role Labeling
132 papers with code • 7 benchmarks • 14 datasets
Semantic role labeling aims to model the predicate-argument structure of a sentence and is often described as answering "Who did what to whom". BIO notation is typically used for semantic role labeling.
Example:
| Housing | starts | are | expected | to | quicken | a | bit | from | August’s | pace |
|---|---|---|---|---|---|---|---|---|---|---|
| B-ARG1 | I-ARG1 | O | O | O | V | B-ARG2 | I-ARG2 | B-ARG3 | I-ARG3 | I-ARG3 |
Benchmarks
These leaderboards are used to track progress in Semantic Role Labeling
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Datasets
FrameNet
OntoNotes 5.0
NomBank
QA-SRL
QA-SRL Bank 2.0
X-SRL
Latest papers with no code
The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLP
I propose a paradigm for scientific progress in NLP centered around developing scalable, data-driven theories of linguistic structure.
CVE-driven Attack Technique Prediction with Semantic Information Extraction and a Domain-specific Language Model
This automated correlation facilitates the creation of labeled data, essential for categorizing novel threat actions into threat functionality classes and TTPs.
Is Argument Structure of Learner Chinese Understandable: A Corpus-Based Analysis
The annotation procedure is guided by the Chinese PropBank specification, which is originally developed to cover first language phenomena.
Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling
A scene-event mapping mechanism is first designed to bridge the gap between the underlying scene structure and the high-level event semantic structure, resulting in an overall hierarchical scene-event (termed ICE) graph structure.
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities
Although we have witnessed impressive progress in Semantic Role Labeling (SRL), most of the research in the area is carried out assuming that the majority of predicates are verbs.
Information Extraction in Domain and Generic Documents: Findings from Heuristic-based and Data-driven Approaches
To fill the gap, in this study, we investigated the accuracy and generalization abilities of heuristic-based searching and data-driven to perform two IE tasks: named entity recognition (NER) and semantic role labeling (SRL) on domain-specific and generic documents with different length.
Persian Semantic Role Labeling Using Transfer Learning and BERT-Based Models
Semantic role labeling (SRL) is the process of detecting the predicate-argument structure of each predicate in a sentence.
Pushing the Limits of ChatGPT on NLP Tasks
In this work, we propose a collection of general modules to address these issues, in an attempt to push the limits of ChatGPT on NLP tasks.
Semantic-aware Dynamic Retrospective-Prospective Reasoning for Event-level Video Question Answering
Specifically, we explicitly use the Semantic Role Labeling (SRL) structure of the question in the dynamic reasoning process where we decide to move to the next frame based on which part of the SRL structure (agent, verb, patient, etc.)
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering
Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field.
