DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data

Abstract

We present an event extraction framework to detect event mentions and extract events from the document-level financial news. Up to now, methods based on supervised learning paradigm gain the highest performance in public datasets (such as ACE 2005 1 , KBP 2015 2). These methods heavily depend on the manually labeled training data. However, in particular areas, such as financial, medical and judicial domains, there is no enough labeled data due to the high cost of data labeling process. Moreover, most of the current methods focus on extracting events from one sentence, but an event is usually expressed by multiple sentences in one document. To solve these problems, we propose a Document-level Chinese Financial Event Extraction (DCFEE) system which can automatically generate a large scaled labeled data and extract events from the whole document. Experimental results demonstrate the effectiveness of it.

Exploring Pre-trained Language Models for Event Extraction and Generation

“…Event Extraction In terms of analysis granularity, there are document-level event extraction (Yang et al, 2018) and sentence-level event extraction (Zeng et al, 2018). We focus on the statistical methods of the latter in this paper.…”

“…However, annotating accurately large amounts of data is a very laborious task. To alleviate the suffering of existing methods from the deficiency of predefined event data, event generation approaches are often used to produce additional events for training (Yang et al, 2018;Zeng et al, 2018;. And distant supervision (Mintz et al, 2009) is a commonly used technique to this end for labeling external corpus.…”

A Survey of Event Extraction From Text

“…Petroni et al [28] defined structures for breaking events, including 7 event types like ''Floods'', ''Storms'', ''Fires'' and etc., as well as their ''5W1H'' attributes, so as to extract breaking events from news reports and social media. Yang et al [29] focused on extracting events in the financial domain to help predicting the stock market, investment decision support and etc. They defined 9 financial event types, like ''Equity Pledge'', ''Equity Freeze'' and etc., as well as their corresponding arguments with different roles.…”

“…Araki and Mitamura [224] utilized the WordNet and Wikipedia to generate new training data. In addition to using the general knowledge bases, some studies have focused on using relevant knowledge bases for domain-specific event extraction [29], [178], [225], [226]. For example, Rao et al [178] used the biological pathway exchange (BioPax) knowledge database, which contains relations between proteins, to expand training data from PubMed 12 central articles.…”

“…For example, Rao et al [178] used the biological pathway exchange (BioPax) knowledge database, which contains relations between proteins, to expand training data from PubMed 12 central articles. In the financial domain, Yang et al [29] utilized a financial event knowledge database for data expansion, which contains 9 common financial event types.…”