2 research outputs found
Weakly-supervised Fine-grained Event Recognition on Social Media Texts for Disaster Management
People increasingly use social media to report emergencies, seek help or
share information during disasters, which makes social networks an important
tool for disaster management. To meet these time-critical needs, we present a
weakly supervised approach for rapidly building high-quality classifiers that
label each individual Twitter message with fine-grained event categories. Most
importantly, we propose a novel method to create high-quality labeled data in a
timely manner that automatically clusters tweets containing an event keyword
and asks a domain expert to disambiguate event word senses and label clusters
quickly. In addition, to process extremely noisy and often rather short
user-generated messages, we enrich tweet representations using preceding
context tweets and reply tweets in building event recognition classifiers. The
evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2
person-hours of human supervision, the rapidly trained weakly supervised
classifiers outperform supervised classifiers trained using more than ten
thousand annotated tweets created in over 50 person-hours.Comment: In Proceedings of the AAAI 2020 (AI for Social Impact Track). Link:
https://aaai.org/ojs/index.php/AAAI/article/view/539
Weakly-supervised Learning Approaches for Event Knowledge Acquisition and Event Detection
Capabilities of detecting events and recognizing temporal, subevent, or causality relations among events can facilitate many applications in natural language understanding. However, supervised learning approaches that previous research mainly uses have two problems. First, due to the limited size of annotated data, supervised systems cannot sufficiently capture diverse contexts to distill universal event knowledge. Second, under certain application circumstances such as event recognition during emergent natural disasters, it is infeasible to spend days or weeks to annotate enough data to train a system. My research aims to use weakly-supervised learning to address these problems and to achieve automatic event knowledge acquisition and event recognition.
In this dissertation, I first introduce three weakly-supervised learning approaches that have been shown effective in acquiring event relational knowledge. Firstly, I explore the observation that regular event pairs show a consistent temporal relation despite of their various contexts, and these rich contexts can be used to train a contextual temporal relation classifier to further recognize new temporal relation knowledge. Secondly, inspired by the double temporality characteristic of narrative texts, I propose a weakly supervised approach that identifies 287k narrative paragraphs using narratology principles and then extract rich temporal event knowledge from identified narratives. Lastly, I develop a subevent knowledge acquisition approach by exploiting two observations that 1) subevents are temporally contained by the parent event and 2) the definitions of the parent event can be used to guide the identification of subevents. I collect rich weak supervision to train a contextual BERT classifier and apply the classifier to identify new subevent knowledge.
Recognizing texts that describe specific categories of events is also challenging due to language ambiguity and diverse descriptions of events. So I also propose a novel method to rapidly build a fine-grained event recognition system on social media texts for disaster management. My method creates high-quality weak supervision based on clustering-assisted word sense disambiguation and enriches tweet message representations using preceding context tweets and reply tweets in building event recognition classifiers