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Found 12 papers, 8 papers with code
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event Extraction
Event extraction in commodity news is a less researched area as compared to generic event extraction.
Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction
We then fine-tuned the spaCy NER tool and validated that having a dataset tailor-made for Malaysian English could improve the performance of NER in Malaysian English significantly.
RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations
While collecting sufficient human-authored data is costly, synthetic conversations provide suitable amounts of data to help mitigate the scarcity of training data, as well as the chance to assess the alignment between LLMs and humans in the awareness of social norms.
How well ChatGPT understand Malaysian English? An Evaluation on Named Entity Recognition and Relation Extraction
Malaysian English is unique as it exhibits morphosyntactic and semantical adaptation from local contexts.
Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer?
Each scenario in the corpus is annotated with a complete IRAC analysis in a semi-structured format so that both machines and legal professionals are able to interpret and understand the annotations.
SocialDial: A Benchmark for Socially-Aware Dialogue Systems
To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.
Cultural Vocal Bursts Intensity Prediction
Synthetic Data Generation
Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach
We build baseline models first by supervised learning and then exploit Transfer Learning methods to boost event extraction model performance despite the limited amount of annotated data and severe class imbalance.
CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction
As part of active learning process, the corpus was used to train basic event extraction models for machine labeling, the resulting models also serve as a validation or as a pilot study demonstrating the use of the corpus in machine learning purposes.
Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event Extraction
Event extraction in commodity news is a less researched area as compared to generic event extraction.
An Annotated Commodity News Corpus for Event Extraction
Commodity News contains a wealth of information such as sum-mary of the recent commodity price movement and notable events that led tothe movement.
Hybrid Models for Aspects Extraction without Labelled Dataset
In this research, two hybrid models are proposed to identify and extract both explicit and implicit aspects, namely TDM-DC and TDM-TED.
