Weak Supervision for Information Extraction

PhD Thesis Proposal 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. 
These two tasks belong to two main categories of tasks in IE: extraction 
tasks and classification tasks, 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 plan to 
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, 13 April 2021

Time:                  	3:00pm - 5:00pm

Zoom Meeting: 
https://hkust.zoom.com.cn/j/91403747618?pwd=Y1FPbHZLVHFKaTNFLzIwRGY4N3Jxdz09

Committee Members:	Dr. Yangqiu Song (Supervisor)
 			Dr. Qifeng Chen (Chairperson)
 			Prof. Fangzhen Lin
 			Dr. Xiaojuan Ma


**** ALL are Welcome ****