LED: A Dataset for Life Event Extraction from Dialogs
- URL: http://arxiv.org/abs/2304.08327v1
- Date: Mon, 17 Apr 2023 14:46:59 GMT
- Title: LED: A Dataset for Life Event Extraction from Dialogs
- Authors: Yi-Pei Chen, An-Zi Yen, Hen-Hsen Huang, Hideki Nakayama, Hsin-Hsi Chen
- Abstract summary: Lifelogging has gained more attention due to its wide applications, such as personalized recommendations or memory assistance.
Life Event Dialog is a dataset containing fine-grained life event annotations on conversational data.
We explore three information extraction (IE) frameworks to address the conversational life event extraction task.
- Score: 57.390999707053915
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Lifelogging has gained more attention due to its wide applications, such as
personalized recommendations or memory assistance. The issues of collecting and
extracting personal life events have emerged. People often share their life
experiences with others through conversations. However, extracting life events
from conversations is rarely explored. In this paper, we present Life Event
Dialog, a dataset containing fine-grained life event annotations on
conversational data. In addition, we initiate a novel conversational life event
extraction task and differentiate the task from the public event extraction or
the life event extraction from other sources like microblogs. We explore three
information extraction (IE) frameworks to address the conversational life event
extraction task: OpenIE, relation extraction, and event extraction. A
comprehensive empirical analysis of the three baselines is established. The
results suggest that the current event extraction model still struggles with
extracting life events from human daily conversations. Our proposed life event
dialog dataset and in-depth analysis of IE frameworks will facilitate future
research on life event extraction from conversations.
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