A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Thomson Reuters' Submission to the FEIII 2017 Challenge Non-scored Tasks
2017
Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets - DSMM'17
Preserved Fulltext
In this paper we describe a machine learning approach to predict roles of extracted SEC triples for the non-scored task of the 2017 FEIII Challenge. ...
INTRODUCTION e main task of the FEIII 2017 challenge [1] was to predict the relevance of triples extracted from 10-K and 10-Q SEC lings. e provided datasets containing the following information for a ...
Table 1 shows the 10 role types and their descriptions. e non-scored tasks then asked us to consider additional tasks built on top of the initial task: 1) apply NLP and Machine Learning to further embellish ...
doi:10.1145/3077240.3077244
dblp:conf/sigmod/RomanUDPK17a
fatcat:udtujvy45bhuhdq5jfyaunw4ye