@inproceedings{weber-steedman-2021-fine,
title = "Fine-grained General Entity Typing in {G}erman using {G}erma{N}et",
author = "Weber, Sabine and
Steedman, Mark",
editor = "Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Logacheva, Varvara and
Jana, Abhik and
Ustalov, Dmitry and
Jansen, Peter",
booktitle = "Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.textgraphs-1.14",
doi = "10.18653/v1/2021.textgraphs-1.14",
pages = "138--143",
abstract = "Fine-grained entity typing is important to tasks like relation extraction and knowledge base construction. We find however, that fine-grained entity typing systems perform poorly on general entities (e.g. {``}ex-president{''}) as compared to named entities (e.g. {``}Barack Obama{''}). This is due to a lack of general entities in existing training data sets. We show that this problem can be mitigated by automatically generating training data from WordNets. We use a German WordNet equivalent, GermaNet, to automatically generate training data for German general entity typing. We use this data to supplement named entity data to train a neural fine-grained entity typing system. This leads to a 10{\%} improvement in accuracy of the prediction of level 1 FIGER types for German general entities, while decreasing named entity type prediction accuracy by only 1{\%}.",
}
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<abstract>Fine-grained entity typing is important to tasks like relation extraction and knowledge base construction. We find however, that fine-grained entity typing systems perform poorly on general entities (e.g. “ex-president”) as compared to named entities (e.g. “Barack Obama”). This is due to a lack of general entities in existing training data sets. We show that this problem can be mitigated by automatically generating training data from WordNets. We use a German WordNet equivalent, GermaNet, to automatically generate training data for German general entity typing. We use this data to supplement named entity data to train a neural fine-grained entity typing system. This leads to a 10% improvement in accuracy of the prediction of level 1 FIGER types for German general entities, while decreasing named entity type prediction accuracy by only 1%.</abstract>
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%0 Conference Proceedings
%T Fine-grained General Entity Typing in German using GermaNet
%A Weber, Sabine
%A Steedman, Mark
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Logacheva, Varvara
%Y Jana, Abhik
%Y Ustalov, Dmitry
%Y Jansen, Peter
%S Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F weber-steedman-2021-fine
%X Fine-grained entity typing is important to tasks like relation extraction and knowledge base construction. We find however, that fine-grained entity typing systems perform poorly on general entities (e.g. “ex-president”) as compared to named entities (e.g. “Barack Obama”). This is due to a lack of general entities in existing training data sets. We show that this problem can be mitigated by automatically generating training data from WordNets. We use a German WordNet equivalent, GermaNet, to automatically generate training data for German general entity typing. We use this data to supplement named entity data to train a neural fine-grained entity typing system. This leads to a 10% improvement in accuracy of the prediction of level 1 FIGER types for German general entities, while decreasing named entity type prediction accuracy by only 1%.
%R 10.18653/v1/2021.textgraphs-1.14
%U https://aclanthology.org/2021.textgraphs-1.14
%U https://doi.org/10.18653/v1/2021.textgraphs-1.14
%P 138-143
Markdown (Informal)
[Fine-grained General Entity Typing in German using GermaNet](https://aclanthology.org/2021.textgraphs-1.14) (Weber & Steedman, TextGraphs 2021)
ACL