SemEval 2018 Task 4: Character Identification on Multiparty Dialogues. Choi, J. D. & Chen, H. Y. In Proceedings of the International Workshop on Semantic Evaluation, of SemEval'18, pages 57–64, New Orleans, LA, 2018.
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Paper Slides abstract bibtex
Paper Paper Slides abstract bibtex Character identification is a task of entity linking that finds the global entity of each personal mention in multiparty dialogue. For this task, the first two seasons of the popular TV show Friends are annotated, comprising a total of 448 dialogues, 15,709 mentions, and 401 entities. The personal mentions are detected from nomi-nals referring to certain characters in the show, and the entities are collected from the list of all characters in those two seasons of the show. This task is challenging because it requires the identification of characters that are mentioned but may not be active during the conversation. Among 90+ participants, four of them submitted their system outputs and showed strengths in different aspects about the task. Thorough analyses of the distributed datasets, system outputs , and comparative studies are also provided. To facilitate the momentum, we create an open-source project for this task and publicly release a larger and cleaner dataset, hoping to support researchers for more enhanced modeling.
@inproceedings{choi:18a,
abstract = {Character identification is a task of entity linking that finds the global entity of each personal mention in multiparty dialogue. For this task, the first two seasons of the popular TV show Friends are annotated, comprising a total of 448 dialogues, 15,709 mentions, and 401 entities. The personal mentions are detected from nomi-nals referring to certain characters in the show, and the entities are collected from the list of all characters in those two seasons of the show. This task is challenging because it requires the identification of characters that are mentioned but may not be active during the conversation. Among 90+ participants, four of them submitted their system outputs and showed strengths in different aspects about the task. Thorough analyses of the distributed datasets, system outputs , and comparative studies are also provided. To facilitate the momentum, we create an open-source project for this task and publicly release a larger and cleaner dataset, hoping to support researchers for more enhanced modeling.},
address = {New Orleans, LA},
author = {Choi, Jinho D. and Chen, Henry Y.},
booktitle = {Proceedings of the International Workshop on Semantic Evaluation},
date-added = {2018-04-30 15:52:26 +0000},
date-modified = {2018-07-02 16:20:52 +0000},
keywords = {emorynlp,character-mining,selected},
pages = {57--64},
series = {SemEval'18},
title = {{SemEval 2018 Task 4: Character Identification on Multiparty Dialogues}},
url = {http://alt.qcri.org/semeval2018/},
url_paper = {http://aclweb.org/anthology/S18-1007.pdf},
url_slides = {https://www.slideshare.net/jchoi7s/semeval-2018-task-4-character-identification-on-multiparty-dialogues},
year = {2018},
Bdsk-Url-1 = {http://alt.qcri.org/semeval2018/}}Downloads: 0
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For this task, the first two seasons of the popular TV show Friends are annotated, comprising a total of 448 dialogues, 15,709 mentions, and 401 entities. The personal mentions are detected from nomi-nals referring to certain characters in the show, and the entities are collected from the list of all characters in those two seasons of the show. This task is challenging because it requires the identification of characters that are mentioned but may not be active during the conversation. Among 90+ participants, four of them submitted their system outputs and showed strengths in different aspects about the task. Thorough analyses of the distributed datasets, system outputs , and comparative studies are also provided. 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