@inproceedings{arefyev-etal-2019-neural,
title = "Neural {GRANN}y at {S}em{E}val-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction",
author = "Arefyev, Nikolay and
Sheludko, Boris and
Davletov, Adis and
Kharchev, Dmitry and
Nevidomsky, Alex and
Panchenko, Alexander",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2004/",
doi = "10.18653/v1/S19-2004",
pages = "31--38",
abstract = "We describe our solutions for semantic frame and role induction subtasks of SemEval 2019 Task 2. Our approaches got the highest scores, and the solution for the frame induction problem officially took the first place. The main contributions of this paper are related to the semantic frame induction problem. We propose a combined approach that employs two different types of vector representations: dense representations from hidden layers of a masked language model, and sparse representations based on substitutes for the target word in the context. The first one better groups synonyms, the second one is better at disambiguating homonyms. Extending the context to include nearby sentences improves the results in both cases. New Hearst-like patterns for verbs are introduced that prove to be effective for frame induction. Finally, we propose an approach to selecting the number of clusters in agglomerative clustering."
}
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%0 Conference Proceedings
%T Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction
%A Arefyev, Nikolay
%A Sheludko, Boris
%A Davletov, Adis
%A Kharchev, Dmitry
%A Nevidomsky, Alex
%A Panchenko, Alexander
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F arefyev-etal-2019-neural
%X We describe our solutions for semantic frame and role induction subtasks of SemEval 2019 Task 2. Our approaches got the highest scores, and the solution for the frame induction problem officially took the first place. The main contributions of this paper are related to the semantic frame induction problem. We propose a combined approach that employs two different types of vector representations: dense representations from hidden layers of a masked language model, and sparse representations based on substitutes for the target word in the context. The first one better groups synonyms, the second one is better at disambiguating homonyms. Extending the context to include nearby sentences improves the results in both cases. New Hearst-like patterns for verbs are introduced that prove to be effective for frame induction. Finally, we propose an approach to selecting the number of clusters in agglomerative clustering.
%R 10.18653/v1/S19-2004
%U https://aclanthology.org/S19-2004/
%U https://doi.org/10.18653/v1/S19-2004
%P 31-38
Markdown (Informal)
[Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction](https://aclanthology.org/S19-2004/) (Arefyev et al., SemEval 2019)
ACL