metadata
language:
- en
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: int64
- name: triplets
list:
- name: object
struct:
- name: surfaceform
dtype: string
- name: uri
dtype: string
- name: predicate
struct:
- name: surfaceform
dtype: string
- name: uri
dtype: string
- name: subject
struct:
- name: surfaceform
dtype: string
- name: uri
dtype: string
- name: entities
list:
- name: surfaceform
dtype: string
- name: uri
dtype: string
- name: relations
list:
- name: surfaceform
dtype: string
- name: uri
dtype: string
- name: linearized_fully_expanded
dtype: string
- name: linearized_subject_collapsed
dtype: string
splits:
- name: train
num_bytes: 117206023
num_examples: 223538
- name: test
num_bytes: 15597162
num_examples: 29620
- name: stratified_test_1K
num_bytes: 608393
num_examples: 1000
- name: val
num_bytes: 522524
num_examples: 980
download_size: 61105204
dataset_size: 133934102
tags:
- wikipedia
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: stratified_test_1K
path: data/stratified_test_1K-*
- split: val
path: data/val-*
Dataset Card for "wiki-nre"
Feature
The Wiki-NRE dataset displays a significant skew in its relations distribution: the top 10 relations constitute 92% of the triplets, with the top 3 alone accounting for 69%.
We have created stratified_test_1K whcih was downscaled from test set to 1,000 samples with balanced distribution of relations
Catalog[Optional]
A corresponding catalog(a list of subset of entities and relations) can be found here: https://huggingface.co/datasets/saibo/wikinre_catalog
Source
@inproceedings{trisedya-etal-2019-neural,
title = "Neural Relation Extraction for Knowledge Base Enrichment",
author = "Trisedya, Bayu Distiawan and
Weikum, Gerhard and
Qi, Jianzhong and
Zhang, Rui",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1023",
doi = "10.18653/v1/P19-1023",
pages = "229--240",
abstract = "We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end-to-end manner. Previous studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into the KB space. This way, NED errors may cause extraction errors that affect the overall precision and recall. To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model. We collect high-quality training data by distant supervision with co-reference resolution and paraphrase detection. We propose an n-gram based attention model that captures multi-word entity names in a sentence. Our model employs jointly learned word and entity embeddings to support named entity disambiguation. Finally, our model uses a modified beam search and a triple classifier to help generate high-quality triples. Our model outperforms state-of-the-art baselines by 15.51{\%} and 8.38{\%} in terms of F1 score on two real-world datasets.",
}