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
ExHVV
Latest papers with no code
Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension
To enhance the interpretability of the model, we propose the semantic role relation table, which represents the semantic relation of the sentence itself and the semantic relations among sentences.
Improving Unsupervised Question Answering via Summarization-Informed Question Generation
Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to train a system to generate a question given a passage and an answer.
Argument Linking: A Survey and Forecast
Semantic role labeling (SRL) -- identifying the semantic relationships between a predicate and other constituents in the same sentence -- is a well-studied task in natural language understanding (NLU).
Deep Learning on Graphs for Natural Language Processing
Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i. e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems.
Neural Unsupervised Semantic Role Labeling
To decompose the task as two argument related subtasks, identification and clustering, we propose a pipeline that correspondingly consists of two neural modules.
Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights
The growing quantity and complexity of data pose challenges for humans to consume information and respond in a timely manner.
Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling
Recent models have shown that incorporating syntactic knowledge into the semantic role labelling (SRL) task leads to a significant improvement.
Conversational Semantic Role Labeling
Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence.
Extracting Semantic Process Information from the Natural Language in Event Logs
While foundational process mining techniques treat such data as sequences of abstract events, more advanced techniques depend on the availability of specific kinds of information, such as resources in organizational mining and business objects in artifact-centric analysis.
Multi-document Summarization using Semantic Role Labeling and Semantic Graph for Indonesian News Article
The decision tree model is employed to identify important PAS.
