A Dataset for Direct Quotation Extraction and Attribution in News Articles.

Overview

DirectQuote - A Dataset for Direct Quotation Extraction and Attribution in News Articles

DirectQuote is a corpus containing 19,760 paragraphs and 10,353 direct quotations manually annotated from online news media.

A quotation is a general notion that covers different kinds of speech, thought, and writing in text (Semino and Short,2004). It is a prominent linguistic device for expressing opinions, statements, and assessments attributed to the speaker (Cappelen and Lepore, 2012). Among all kinds of quotations, the entire content of the direct quotation (O’Keefe et al.,2013) is in quotation marks, which means that what the speaker said is transcribed verbatim.

Task Definition

Quotation extractionis defined as extracting reported speech from a third party in the text, also known as reportedspeech extraction. Quotation attribution refers to determining the speaker of the quotation. When annotating speakers, we ensure that valid speakers should be able to belinked to a person entity in a named entity library. Among them, simple patterns are removed to increase the diversity of the corpus.

Data

Region Name Numbers
U.S. Associated Press 438
Cable News Network 627
American Broadcasting Company 240
New York Times 5,642
CBS Broadcasting 4,890
UK British Broadcasting Corporation 926
Reuters 5,836
The Guardian 4,302
Canada The Globe and Mail 1,955
The Star 13,769
New Zealand NZ Herald 115
Australia Australian Broadcasting Corporation 312
Sydney Morning Herald 93

We select representative and multiple news sources across the political spectrum, including 13 well-known online news media from five major English-speaking countries. The corpus adopts the format consistent with CoNLL 2003. We use IOB1 format in the corpus. Raw texts are tokenized by whitespace tokenizer. Every word is classified into the following lables:

  • LeftSpeaker Quotation, the corresponding speaker is in the preceding text
  • RightSpeaker Quotation, the corresponding speaker is in the following text
  • Unknown Quotation, no corresponding speaker
  • Speaker Speaker
  • Out Neither

Statistics

Numbers
News Article 39,153
Paragraph 19,760
Quotation 10,353
Time 2020.09-2021.03

Reference

DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles, Yuanchi Zhang, Yang Liu

Owner
THUNLP-MT
Machine Translation Group, Natural Language Processing Lab at Tsinghua University (THUNLP). Please refer to https://github.com/thunlp for more NLP resources.
THUNLP-MT
Code for the paper "JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design"

JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design This repository contains code for the paper: JA

Aspuru-Guzik group repo 55 Nov 29, 2022
[NeurIPS 2021] COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

COCO-LM This repository contains the scripts for fine-tuning COCO-LM pretrained models on GLUE and SQuAD 2.0 benchmarks. Paper: COCO-LM: Correcting an

Microsoft 106 Dec 12, 2022
Demonstration of transfer of knowledge and generalization with distillation

Distilling-the-Knowledge-in-a-Neural-Network This is an implementation of a part of the paper "Distilling the Knowledge in a Neural Network" (https://

26 Nov 25, 2022
Automated Attendance Project Using Face Recognition

dependencies for project: cmake 3.22.1 dlib 19.22.1 face-recognition 1.3.0 openc

Rohail Taha 1 Jan 09, 2022
Bayesian Neural Networks in PyTorch

We present the new scheme to compute Monte Carlo estimator in Bayesian VI settings with almost no memory cost in GPU, regardles of the number of sampl

Jurijs Nazarovs 7 May 03, 2022
Codebase for "Revisiting spatio-temporal layouts for compositional action recognition" (Oral at BMVC 2021).

Revisiting spatio-temporal layouts for compositional action recognition Codebase for "Revisiting spatio-temporal layouts for compositional action reco

Gorjan 20 Dec 15, 2022
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020).

NHDRRNet-PyTorch This is the PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020). 0. Differences between Original Paper and

Yutong Zhang 1 Mar 01, 2022
Custom IMDB Dataset is extracted between 2020-2021 and custom distilBERT model is trained for movie success probability prediction

IMDB Success Predictor Project involves Web Scraping custom IMDB data between 2020 and 2021 of 10000 movies and shows sorted by number of votes ,fine

Gautam Diwan 1 Jan 18, 2022
[NeurIPS 2021] Code for Unsupervised Learning of Compositional Energy Concepts

Unsupervised Learning of Compositional Energy Concepts This is the pytorch code for the paper Unsupervised Learning of Compositional Energy Concepts.

45 Nov 30, 2022
MLSpace: Hassle-free machine learning & deep learning development

MLSpace: Hassle-free machine learning & deep learning development

abhishek thakur 293 Jan 03, 2023
Implementation of popular SOTA self-supervised learning algorithms as Fastai Callbacks.

Self Supervised Learning with Fastai Implementation of popular SOTA self-supervised learning algorithms as Fastai Callbacks. Install pip install self-

Kerem Turgutlu 276 Dec 23, 2022
This is the official pytorch implementation of the BoxEL for the description logic EL++

BoxEL: Box EL++ Embedding This is the official pytorch implementation of the BoxEL for the description logic EL++. BoxEL++ is a geometric approach bas

1 Nov 03, 2022
Self-supervised learning on Graph Representation Learning (node-level task)

graph_SSL Self-supervised learning on Graph Representation Learning (node-level task) How to run the code To run GRACE, sh run_GRACE.sh To run GCA, sh

Namkyeong Lee 3 Dec 31, 2021
Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.

EfficientZero (NeurIPS 2021) Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021. Thank you for you

Weirui Ye 671 Jan 03, 2023
The source code for 'Noisy-Labeled NER with Confidence Estimation' accepted by NAACL 2021

Kun Liu*, Yao Fu*, Chuanqi Tan, Mosha Chen, Ningyu Zhang, Songfang Huang, Sheng Gao. Noisy-Labeled NER with Confidence Estimation. NAACL 2021. [arxiv]

30 Nov 12, 2022
Deep Illuminator is a data augmentation tool designed for image relighting. It can be used to easily and efficiently generate a wide range of illumination variants of a single image.

Deep Illuminator Deep Illuminator is a data augmentation tool designed for image relighting. It can be used to easily and efficiently generate a wide

George Chogovadze 52 Nov 29, 2022
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs Abstract: Image-to-image translation has recently achieved re

yaxingwang 23 Apr 14, 2022
vit for few-shot classification

Few-Shot ViT Requirements PyTorch (= 1.9) TorchVision timm (latest) einops tqdm numpy scikit-learn scipy argparse tensorboardx Pretrained Checkpoints

Martin Dong 26 Nov 30, 2022