@inproceedings{jiang-riloff-2021-exploiting,
title = "Exploiting Definitions for Frame Identification",
author = "Jiang, Tianyu and
Riloff, Ellen",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.206",
doi = "10.18653/v1/2021.eacl-main.206",
pages = "2429--2434",
abstract = "Frame identification is one of the key challenges for frame-semantic parsing. The goal of this task is to determine which frame best captures the meaning of a target word or phrase in a sentence. We present a new model for frame identification that uses a pre-trained transformer model to generate representations for frames and lexical units (senses) using their formal definitions in FrameNet. Our frame identification model assesses the suitability of a frame for a target word in a sentence based on the semantic coherence of their meanings. We evaluate our model on three data sets and show that it consistently achieves better performance than previous systems.",
}
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<abstract>Frame identification is one of the key challenges for frame-semantic parsing. The goal of this task is to determine which frame best captures the meaning of a target word or phrase in a sentence. We present a new model for frame identification that uses a pre-trained transformer model to generate representations for frames and lexical units (senses) using their formal definitions in FrameNet. Our frame identification model assesses the suitability of a frame for a target word in a sentence based on the semantic coherence of their meanings. We evaluate our model on three data sets and show that it consistently achieves better performance than previous systems.</abstract>
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%0 Conference Proceedings
%T Exploiting Definitions for Frame Identification
%A Jiang, Tianyu
%A Riloff, Ellen
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F jiang-riloff-2021-exploiting
%X Frame identification is one of the key challenges for frame-semantic parsing. The goal of this task is to determine which frame best captures the meaning of a target word or phrase in a sentence. We present a new model for frame identification that uses a pre-trained transformer model to generate representations for frames and lexical units (senses) using their formal definitions in FrameNet. Our frame identification model assesses the suitability of a frame for a target word in a sentence based on the semantic coherence of their meanings. We evaluate our model on three data sets and show that it consistently achieves better performance than previous systems.
%R 10.18653/v1/2021.eacl-main.206
%U https://aclanthology.org/2021.eacl-main.206
%U https://doi.org/10.18653/v1/2021.eacl-main.206
%P 2429-2434
Markdown (Informal)
[Exploiting Definitions for Frame Identification](https://aclanthology.org/2021.eacl-main.206) (Jiang & Riloff, EACL 2021)
ACL
- Tianyu Jiang and Ellen Riloff. 2021. Exploiting Definitions for Frame Identification. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2429–2434, Online. Association for Computational Linguistics.