KNU CI System at SemEval-2018 Task4: Character Identification by Solving Sequence-Labeling Problem

Cheoneum Park, Heejun Song, Changki Lee


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.
Anthology ID:
S18-1107
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
655–659
Language:
URL:
https://www.aclweb.org/anthology/S18-1107
DOI:
10.18653/v1/S18-1107
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/S18-1107.pdf