@inproceedings{park-etal-2018-knu,
title = "{KNU} {CI} System at {S}em{E}val-2018 Task4: Character Identification by Solving Sequence-Labeling Problem",
author = "Park, Cheoneum and
Song, Heejun and
Lee, Changki",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1107",
doi = "10.18653/v1/S18-1107",
pages = "655--659",
abstract = "Character identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, we define a sequence-labeling problem to solve character identification, and propose an attention-based recurrent neural network (RNN) encoder{--}decoder model. The in-put document for character identification on multiparty dialogues consists of several conversations, which increase the length of the input sequence. The RNN encoder{--}decoder model suffers from poor performance when the length of the input sequence is long. To solve this problem, we propose applying position encoding and the self-matching network to the RNN encoder{--}decoder model. Our experimental results demonstrate that of the four models proposed, Model 2 showed an F1 score of 86.00{\%} and a label accuracy of 85.10{\%} at the scene-level.",
}
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<abstract>Character identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, we define a sequence-labeling problem to solve character identification, and propose an attention-based recurrent neural network (RNN) encoder–decoder model. The in-put document for character identification on multiparty dialogues consists of several conversations, which increase the length of the input sequence. The RNN encoder–decoder model suffers from poor performance when the length of the input sequence is long. To solve this problem, we propose applying position encoding and the self-matching network to the RNN encoder–decoder model. Our experimental results demonstrate that of the four models proposed, Model 2 showed an F1 score of 86.00% and a label accuracy of 85.10% at the scene-level.</abstract>
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%0 Conference Proceedings
%T KNU CI System at SemEval-2018 Task4: Character Identification by Solving Sequence-Labeling Problem
%A Park, Cheoneum
%A Song, Heejun
%A Lee, Changki
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F park-etal-2018-knu
%X Character identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, we define a sequence-labeling problem to solve character identification, and propose an attention-based recurrent neural network (RNN) encoder–decoder model. The in-put document for character identification on multiparty dialogues consists of several conversations, which increase the length of the input sequence. The RNN encoder–decoder model suffers from poor performance when the length of the input sequence is long. To solve this problem, we propose applying position encoding and the self-matching network to the RNN encoder–decoder model. Our experimental results demonstrate that of the four models proposed, Model 2 showed an F1 score of 86.00% and a label accuracy of 85.10% at the scene-level.
%R 10.18653/v1/S18-1107
%U https://aclanthology.org/S18-1107
%U https://doi.org/10.18653/v1/S18-1107
%P 655-659
Markdown (Informal)
[KNU CI System at SemEval-2018 Task4: Character Identification by Solving Sequence-Labeling Problem](https://aclanthology.org/S18-1107) (Park et al., SemEval 2018)
ACL