An Effective System for Multi-format Information Extraction
Yaduo Liu, Longhui Zhang, Shujuan Yin, Xiaofeng Zhao, Feiliang Ren

TL;DR
This paper presents a comprehensive system for multi-format information extraction, converting various subtasks into suitable formats and integrating models for relation and event extraction, achieving competitive results in a challenge.
Contribution
It introduces novel conversion and multi-task learning techniques for relation and event extraction across multiple formats, advancing the state-of-the-art in multi-format IE systems.
Findings
Achieved top-4 ranking in the challenge
Relation extraction F1 score of 79.887%
Event extraction F1 scores of 85.179% (sentence-level) and 70.828% (document-level)
Abstract
The multi-format information extraction task in the 2021 Language and Intelligence Challenge is designed to comprehensively evaluate information extraction from different dimensions. It consists of an multiple slots relation extraction subtask and two event extraction subtasks that extract events from both sentence-level and document-level. Here we describe our system for this multi-format information extraction competition task. Specifically, for the relation extraction subtask, we convert it to a traditional triple extraction task and design a voting based method that makes full use of existing models. For the sentence-level event extraction subtask, we convert it to a NER task and use a pointer labeling based method for extraction. Furthermore, considering the annotated trigger information may be helpful for event extraction, we design an auxiliary trigger recognition model and use…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Web Data Mining and Analysis
