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
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning
Unfortunately, this requires formatting them into specialized augmented format unknown to the base pretrained language model (PLMs) necessitating finetuning to the target format.
Extracting Entities of Interest from Comparative Product Reviews
jatinarora2702/Review-Information-Extraction
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Conference on Information and Knowledge Management (CIKM) 2017
This paper presents a deep learning based approach to extract product comparison information out of user reviews on various e-commerce websites.
Speaker attribution in German parliamentary debates with QLoRA-adapted large language models
The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis.
Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model
pauli31/srl-aspect-based-sentiment
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We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state.
Discovering collective narratives shifts in online discussions
Narrative is a foundation of human cognition and decision making.
Semantic Role Labeling Guided Out-of-distribution Detection
cytai/srlood
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Identifying unexpected domain-shifted instances in natural language processing is crucial in real-world applications.
Learning Semantic Role Labeling from Compatible Label Sequences
utahnlp/marginal_srl_with_semlink
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In this paper, we eliminate such issue with a framework that jointly models VerbNet and PropBank labels as one sequence.
Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization
kenchan0226/finegrainedfact
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Motivated by how humans inspect factual inconsistency in summaries, we propose an interpretable fine-grained inconsistency detection model, FineGrainFact, which explicitly represents the facts in the documents and summaries with semantic frames extracted by semantic role labeling, and highlights the related semantic frames to predict inconsistency.
Evaluating Factual Consistency of Texts with Semantic Role Labeling
We introduce SRLScore, a reference-free evaluation metric designed with text summarization in mind.
Extracting Victim Counts from Text
We cast victim count extraction as a question answering (QA) task with a regression or classification objective.
