Datasets for generic relation extraction

Abstract

A vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g. PersonW works for OrganisationX, GeneY encodes ProteinZ ) and recode it in a format such as a relational database or RDF triplestore that can be more effectively used for querying and automated reasoning. A number of resources have been developed for training and evaluating automatic systems for relation extraction in different domains. However, comparative evaluation is impeded by the fact that these corpora use different markup formats and notions of what constitutes a relation. We describe the preparation of corpora for comparative evaluation of relation extraction across domains based on the publicly available ACE 2004, ACE 2005 and BioInfer data sets. We present a common document type using token standoff and including detailed linguistic markup, while maintaining all information in the original annotation. The subsequent reannotation process normalises the two data sets so that they comply with a notion of relation that is intuitive, simple and informed by the semantic web. For the ACE data, we describe an automatic process that automatically converts many relations involving nested, nominal entity mentions to relations involving non-nested, named or pronominal entity mentions. For example, the first entity is mapped from 'one' to 'Amidu Berry' in the membership relation described in 'Amidu Berry, one half of PBS'. Moreover, we describe a comparably reannotated version of the BioInfer corpus that flattens nested relations, maps part-whole to part-part relations and maps n-ary to binary relations. Finally, we summarise experiments that compare approaches to generic relation extraction, a knowledge discovery task that uses minimally supervised techniques to achieve maximally portable extractors. These experiments illustrate the utility of the corpora. 1

Text mining brain imaging reports

“…the ANNIE NER tagger which is part of GATE [5]. Relation extraction is often included as a subtask in text mining applications [6] with approaches to it ranging from rule-based through supervised to unsupervised machine learning.…”

Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

“…ADE-EXT (Adverse Drug Effect corpus, extended) (Gurulingappa et al, 2012) consists of MEDLINE case reports, annotated with drugs and conditions (e.g., diseases, signs and symptoms), along with untyped relationships between them. reACE (Edinburgh Regularized Automatic Content Extraction) (Hachey et al, 2012) consists of English broadcast news and newswire annotated with organization, person, fvw (facility, vehicle or weapon) and gpl (geographical, political or location) entities along with relationships between them. Relationships are classified in five types: general-affiliation, organisation-affiliation, partwhole, personal-social and agent-artifact.…”

“…First, we show that, compared to a baseline Convolutional Neural Network (CNN)-based model, a syntax-based model (i.e., the TreeLSTM model) can better benefit from a TL strategy, even with very dissimilar additional source data. We conduct our experiments with two biomedical RE tasks and relatively small associated corpora, SNPPhenA (Bokharaeian et al, 2017) and EU-ADR (van Mulligen et al, 2012) as target corpora and three larger RE corpora, Semeval 2013DDI (Herrero-Zazo et al, 2013, ADE-EXT (Gurulingappa et al, 2012), reACE (Hachey et al, 2012) as source corpora. Second, we propose a syntax-based analysis, using both quantitative criteria and qualitative observations, to better understand the role of syntactic features in the TL behavior.…”

Fingerprint Matrices: Uncovering the dynamics of social networks in prose literature

“…Relation extraction approaches can be classified in various ways. Knowledge engineering approaches (e.g., rule-based, linguistic based), learning approaches (e.g., statistical, machine learning, bootstrapping) and hybrid ones; for a general review of relation extraction techniques see [Hac09].…”