An Effective System for Multi-format Information Extraction
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 fr...
Gespeichert in:
Hauptverfasser: | , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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 the
multi-task learning mechanism to integrate the trigger features into the event
extraction model. For the document-level event extraction subtask, we design an
Encoder-Decoder based method and propose a Transformer-alike decoder.
Finally,our system ranks No.4 on the test set leader-board of this multi-format
information extraction task, and its F1 scores for the subtasks of relation
extraction, event extractions of sentence-level and document-level are 79.887%,
85.179%, and 70.828% respectively. The codes of our model are available at
{https://github.com/neukg/MultiIE}. |
---|---|
DOI: | 10.48550/arxiv.2108.06957 |