Hugging Face's logo

Datasets:
relbert
/
lexical_relation_classification

Modalities:
Text
Languages:
English
Size:
100K - 1M
Libraries:
Datasets
License:
Dataset card Data Studio Files Community
lexical_relation_classification / README.md
asahi417's picture
asahi417
init
9862d1e
preview code
|
raw
history blame contribute delete
2.92 kB
metadata
language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - n<1K
pretty_name: Lexical Relation Classification

Dataset Card for "relbert/lexical_relation_classification"

Dataset Description

Dataset Summary

Five different datasets (BLESS, CogALexV, EVALution, K&H+N, ROOT09) for lexical relation classification used in SphereRE.

Dataset Summary

This dataset contains 5 different word analogy questions used in Analogy Language Model.

name train validation test
BLESS 18582 1327 6637
CogALexV 3054 - 4260
EVALution 5160 372 1846
K&H+N 40256 2876 14377
ROOT09 8933 638 3191

Dataset Structure

Data Instances

An example looks as follows.

{"head": "turtle", "tail": "live", "relation": "event"}

The stem and tail are the word pair and relation is the corresponding relation label.

Citation Information 

@inproceedings{wang-etal-2019-spherere,
    title = "{S}phere{RE}: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings",
    author = "Wang, Chengyu  and
      He, Xiaofeng  and
      Zhou, Aoying",
    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/P19-1169",
    doi = "10.18653/v1/P19-1169",
    pages = "1727--1737",
    abstract = "Lexical relations describe how meanings of terms relate to each other. Typical examples include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and also challenging due to the lack of contextual signals to discriminate between such relations. In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE). Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated. Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.",
}

LICENSE

The LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use.