@inproceedings{zhou-etal-2018-zero,
title = "Zero-Shot Open Entity Typing as Type-Compatible Grounding",
author = "Zhou, Ben and
Khashabi, Daniel and
Tsai, Chen-Tse and
Roth, Dan",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1231",
doi = "10.18653/v1/D18-1231",
pages = "2065--2076",
abstract = "The problem of entity-typing has been studied predominantly as a supervised learning problems, mostly with task-specific annotations (for coarse types) and sometimes with distant supervision (for fine types). While such approaches have strong performance within datasets they often lack the flexibility to transfer across text genres and to generalize to new type taxonomies. In this work we propose a zero-shot entity typing approach that requires no annotated data and can flexibly identify newly defined types. Given a type taxonomy, the entries of which we define as Boolean functions of freebase {``}types,{''} we ground a given mention to a set of \textit{type-compatible} Wikipedia entries, and then infer the target mention{'}s type using an inference algorithm that makes use of the types of these entries. We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and on a dataset in the biological domain. Our system is shown to be competitive with state-of-the-art supervised NER systems, and to outperform them on out-of-training datasets. We also show that our system significantly outperforms other zero-shot fine typing systems.",
}
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<abstract>The problem of entity-typing has been studied predominantly as a supervised learning problems, mostly with task-specific annotations (for coarse types) and sometimes with distant supervision (for fine types). While such approaches have strong performance within datasets they often lack the flexibility to transfer across text genres and to generalize to new type taxonomies. In this work we propose a zero-shot entity typing approach that requires no annotated data and can flexibly identify newly defined types. Given a type taxonomy, the entries of which we define as Boolean functions of freebase “types,” we ground a given mention to a set of type-compatible Wikipedia entries, and then infer the target mention’s type using an inference algorithm that makes use of the types of these entries. We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and on a dataset in the biological domain. Our system is shown to be competitive with state-of-the-art supervised NER systems, and to outperform them on out-of-training datasets. We also show that our system significantly outperforms other zero-shot fine typing systems.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Open Entity Typing as Type-Compatible Grounding
%A Zhou, Ben
%A Khashabi, Daniel
%A Tsai, Chen-Tse
%A Roth, Dan
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zhou-etal-2018-zero
%X The problem of entity-typing has been studied predominantly as a supervised learning problems, mostly with task-specific annotations (for coarse types) and sometimes with distant supervision (for fine types). While such approaches have strong performance within datasets they often lack the flexibility to transfer across text genres and to generalize to new type taxonomies. In this work we propose a zero-shot entity typing approach that requires no annotated data and can flexibly identify newly defined types. Given a type taxonomy, the entries of which we define as Boolean functions of freebase “types,” we ground a given mention to a set of type-compatible Wikipedia entries, and then infer the target mention’s type using an inference algorithm that makes use of the types of these entries. We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and on a dataset in the biological domain. Our system is shown to be competitive with state-of-the-art supervised NER systems, and to outperform them on out-of-training datasets. We also show that our system significantly outperforms other zero-shot fine typing systems.
%R 10.18653/v1/D18-1231
%U https://aclanthology.org/D18-1231
%U https://doi.org/10.18653/v1/D18-1231
%P 2065-2076
Markdown (Informal)
[Zero-Shot Open Entity Typing as Type-Compatible Grounding](https://aclanthology.org/D18-1231) (Zhou et al., EMNLP 2018)
ACL
- Ben Zhou, Daniel Khashabi, Chen-Tse Tsai, and Dan Roth. 2018. Zero-Shot Open Entity Typing as Type-Compatible Grounding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2065–2076, Brussels, Belgium. Association for Computational Linguistics.