@inproceedings{c82adfb68d4d4b729fc554f65d436d61,
title = "Detecting Personal Life Events from Twitter by Multi-Task LSTM",
abstract = "People are used to log their life on the social media platform. Life event can be expressed explicitly or implicitly in a text description. However, a description does not always contain life events related to a specific individual. To tell if there exist any life events and further know their categories is indispensable for event retrieval. This paper explores various LSTM models to detect and classify life events in tweets. Experiments show that the proposed Multi-Task LSTM model with attention achieves the best performance.",
keywords = "lifelogging, personal event detection, social media",
author = "Yen, {An Zi} and Huang, {Hen Hsen} and Chen, {Hsin Hsi}",
note = "Publisher Copyright: {\textcopyright} 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.; 27th International World Wide Web, WWW 2018 ; Conference date: 23-04-2018 Through 27-04-2018",
year = "2018",
month = apr,
day = "23",
doi = "10.1145/3184558.3186909",
language = "English",
series = "The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018",
publisher = "Association for Computing Machinery, Inc",
pages = "21--22",
booktitle = "The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018",
}