Abstract

Information Extraction (IE) from scientific texts can be used to guide readers to the central information in scientific documents. But narrow IE systems extract only a fraction of the information captured, and Open IE systems do not perform well on the long and complex sentences encountered in scientific texts. In this work we combine the output of both types of systems to achieve Semi-Open Relation Extraction, a new task that we explore in the Biology domain. First, we present the Focused Open Biological Information Extraction (FOBIE) dataset and use FOBIE to train a state-of-the-art narrow scientific IE system to extract trade-off relations and arguments that are central to biology texts. We then run both the narrow IE system and a state-of-the-art Open IE system on a corpus of 10K open-access scientific biological texts. We show that a significant amount (65%) of erroneous and uninformative Open IE extractions can be filtered using narrow IE extractions. Furthermore, we show that the retained extractions are significantly more often informative to a reader.

Highlights

  • Identifying the central theme and concepts in scientific texts is a time-consuming task for experts and a hard task for laymen (Alper et al, 2004; ElArini and Guestrin, 2011; Pain, 2016)

  • Considering the poor quality of Open IE (OIE) extractions, we propose presenting a reader with the sentences that entail the filtered OIE extractions

  • We introduce the task of Semi-Open Relation Extraction (SORE) on scientific texts and the Focused Open Biological Information Extraction (FOBIE) dataset

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Summary

Introduction

Identifying the central theme and concepts in scientific texts is a time-consuming task for experts and a hard task for laymen (Alper et al, 2004; ElArini and Guestrin, 2011; Pain, 2016) This problem is even more pronounced in inter-disciplinary fields of study, where experts in a target domain often lack the deeper knowledge of a source domain (Carr et al, 2018). A major issue is that engineers (target domain) know little biology (source domain) or characteristics of plants or animals (Vattam and Goel, 2013) This domainmismatch complicates searching for and reasoning over relevant scientific information, rendering biomimetics adventitious and solutions serendipitous (Kruiper et al, 2018).

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