Weak Supervision for Information Extraction

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Weak Supervision for Information Extraction"

By

Mr. Hongliang DAI


Abstract

Deep learning models have gained much success in Information Extraction 
(IE) from text. Such models usually require a large number of labeled 
samples to train. Since human annotation can be difficult and time 
consuming, automatically generated weak supervision is widely leveraged.

We investigate the creation and the use of weak annotations for IE with 
two tasks: Aspect and Opinion Term Extraction (AOTE), and Entity Typing. 
They belong to the two kinds of operations that an IE system needs to 
carry out, respectively.

First, we are interested in generating context-dependent weak annotations 
without much human effort. For AOTE, we propose an approach to annotating 
a large number of training samples with automatic annotation rules. The 
rules are mined from a small human labeled sample set, and thus do not 
need to be designed manually. For the task of entity typing, we propose an 
approach that generates entity type labels by exploiting a pretrained 
masked language model.

For the use of the generated weak annotations, we consider two settings. 
One setting is that only a set of weakly labeled samples is available. 
Under this setting, we propose to improve the performance of an entity 
typing model by leveraging external knowledge. Another setting is that 
both a set of weakly labeled samples and a small set of human annotated 
samples are available. We show that pretraining neural models with weak 
supervision, then fine-tuning them on human annotated data can yield good 
results. Then, with the task of entity typing, we investigate a framework 
that obtains a better performing system by first training multiple models 
with the weakly labeled data, then stacking them with the help of a small 
high quality sample set.


Date:			Tuesday, 29 June 2021

Time:			2:00pm - 4:00pm

Zoom Meeting: 
https://hkust.zoom.us/j/98736886906?pwd=TThlenNFck5WUm1ZK3RORHlIUERUZz09

Chairperson:		Prof. Yong HUANG (CHEM)

Committee Members:	Prof. Yangqiu SONG (Supervisor)
 			Prof. Fangzhen LIN
 			Prof. Xiaojuan MA
 			Prof. Yi YANG (ISOM)
 			Prof. Wei LU (Singapore Univ of Tech & Design)


**** ALL are Welcome ****