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
Most implemented papers
Neural Semantic Role Labeling with Dependency Path Embeddings
This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques.
Situation Recognition: Visual Semantic Role Labeling for Image Understanding
my89/imSitu
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CVPR 2016
This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e. g., clipping), (2) the participating actors, objects, substances, and locations (e. g., man, shears, sheep, wool, and field) and most importantly (3) the roles these participants play in the activity (e. g., the man is clipping, the shears are his tool, the wool is being clipped from the sheep, and the clipping is in a field).
BioMedLAT Corpus: Annotation of the Lexical Answer Type for Biomedical Questions
Question answering (QA) systems need to provide exact answers for the questions that are posed to the system.
Better call Saul: Flexible Programming for Learning and Inference in NLP
We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP).
Deep Semantic Role Labeling: What Works and What's Next
We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations.
Dict2vec : Learning Word Embeddings using Lexical Dictionaries
Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks.
Deep Semantic Role Labeling with Self-Attention
XMUNLP/Tagger
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Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied.
AllenNLP: A Deep Semantic Natural Language Processing Platform
This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding.
