Towards Building a Knowledge Base of Monetary Transactions from a News Collection

TL;DR

This paper presents a method for extracting and consolidating structured economic event data from news articles to build a financial knowledge base, improving accuracy with supervised learning.

Contribution

It introduces a joint extraction approach for event attributes and a confidence ranking method, enhancing data accuracy over baseline techniques.

Findings

Achieved 25% improvement in F1-score over baseline methods.

Effectively consolidates multiple reports of the same event.

Supports semi-automatic population of a financial knowledge base.

Abstract

We address the problem of extracting structured representations of economic events from a large corpus of news articles, using a combination of natural language processing and machine learning techniques. The developed techniques allow for semi-automatic population of a financial knowledge base, which, in turn, may be used to support a range of data mining and exploration tasks. The key challenge we face in this domain is that the same event is often reported multiple times, with varying correctness of details. We address this challenge by first collecting all information pertinent to a given event from the entire corpus, then considering all possible representations of the event, and finally, using a supervised learning method, to rank these representations by the associated confidence scores. A main innovative element of our approach is that it jointly extracts and stores all attributes of the event as a single representation (quintuple). Using a purpose-built test set we demonstrate that our supervised learning approach can achieve 25% improvement in F1-score over baseline methods that consider the earliest, the latest or the most frequent reporting of the event.