Research On Data Sparsity-Oriented Event Temporal Relation Extraction

As a crucial component of relation extraction in the field of Natural Language Processing(NLP),event temporal relation extraction has broad application prospects in many fields,such as medicine and finance.It also benefits lots of downstream NLP tasks that require deep understanding on the temporal information of natural texts,such as question answering,timeline construction and summarization.Previous studies often suffer from the issues of data scarcity and imbalanced instances,which may cause over-fitting in model training step and bring performance decrease.To relieve above issues and mine more temporal information from limited resources,this dissertation has carried out research work as follows:(1)Event temporal relation extraction based on negative sample re-annotationThe issues of the imbalanced distribution of instances,and the ambiguity of vague instances in existing corpora have severe performance impact on event temporal relation extraction system.To relieve the issues,this dissertation proposes a negative sample reannotation method.Different from previous studies that use single-label temporal relation,this method define the relation as multi-label form,making ambiguous relations be compatible.Besides,our fine-grained re-annotation on the negative samples helps exploit more definite temporal clues from the vague instances.Due to the huge difference between our proposal and previous annotation,we propose two corpus usage strategies,i.e.,multi-to-single and extra label mapping methods,aiming to fully use the annotated information.Experimental results show that our models on the annotation scheme and mapping methods outperform previous work significantly.(2)DCT-centered temporal relation extractionPrevious studies on temporal relation extraction mostly only focus on event-event relation,while the temporal relations of event-timex and event-DCT are often ignored.To resolve this problem,this dissertation proposes a DCT-centered temporal relation extraction method.The method centers on the core temporal clue of DCT,and utilizes multi-task learning framework,which combines several temporal relation extraction tasks into a unified DCT-centered neural network to make the best use of the limited training resource.This method takes advantage of different type of samples,moreover,DCT can act as a hub to connect the whole document,providing more direct temporal clues for the main event-event temporal relation extraction task throughout the training and inference processes.Experimental results show that the proposed method outperforms previous work significantly.(3)Event temporal relation extraction based on time anchoring and negative denoising.Previous neural network models often depend on related NLP tasks,causing high coupling and complexity.To address this issues,this dissertation proposes an efficient event temporal relation extraction model based on time anchoring and negative denoising.In detail,according to the definition of the temporal relations,we design a novel anchor loss for time anchor,besides,we make events learn from the value of time expressions and DCT of the document in a multi-task learning framework.Moreover,in order to decrease the interference of vague instances in training step,we propose a negative denoising mechanism that dynamically adapts the learning weight of negative samples during training process.Experimental results show that our proposal can alleviate the problem of data imbalance and provide performance gains for multiple temporal relation extraction tasks.This dissertation focuses on the event temporal relation extraction,and proposes several efficient methods to improve the task performance and provide reference for future research.