@inproceedings{anwar-etal-2019-hhmm,
title = "{HHMM} at {S}em{E}val-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings",
author = "Anwar, Saba and
Ustalov, Dmitry and
Arefyev, Nikolay and
Ponzetto, Simone Paolo and
Biemann, Chris 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/S19-2018",
doi = "10.18653/v1/S19-2018",
pages = "125--129",
abstract = "We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (Qasem-iZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings and role labeling by combining these embeddings with syntactical features. A simple combination of these steps shows very competitive results and can be extended to process other datasets and languages.",
}
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%0 Conference Proceedings
%T HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
%A Anwar, Saba
%A Ustalov, Dmitry
%A Arefyev, Nikolay
%A Ponzetto, Simone Paolo
%A Biemann, Chris
%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 anwar-etal-2019-hhmm
%X We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (Qasem-iZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings and role labeling by combining these embeddings with syntactical features. A simple combination of these steps shows very competitive results and can be extended to process other datasets and languages.
%R 10.18653/v1/S19-2018
%U https://aclanthology.org/S19-2018
%U https://doi.org/10.18653/v1/S19-2018
%P 125-129
Markdown (Informal)
[HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings](https://aclanthology.org/S19-2018) (Anwar et al., SemEval 2019)
ACL