Dataset Card for "relbert/lexical_relation_classification"

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.