9 research outputs found
Analysing Errors of Open Information Extraction Systems
We report results on benchmarking Open Information Extraction (OIE) systems
using RelVis, a toolkit for benchmarking Open Information Extraction systems.
Our comprehensive benchmark contains three data sets from the news domain and
one data set from Wikipedia with overall 4522 labeled sentences and 11243
binary or n-ary OIE relations. In our analysis on these data sets we compared
the performance of four popular OIE systems, ClausIE, OpenIE 4.2, Stanford
OpenIE and PredPatt. In addition, we evaluated the impact of five common error
classes on a subset of 749 n-ary tuples. From our deep analysis we unreveal
important research directions for a next generation of OIE systems.Comment: Accepted at Building Linguistically Generalizable NLP Systems at
EMNLP 201
A Survey on Open Information Extraction
We provide a detailed overview of the various approaches that were proposed
to date to solve the task of Open Information Extraction. We present the major
challenges that such systems face, show the evolution of the suggested
approaches over time and depict the specific issues they address. In addition,
we provide a critique of the commonly applied evaluation procedures for
assessing the performance of Open IE systems and highlight some directions for
future work.Comment: 27th International Conference on Computational Linguistics (COLING
2018
Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications
With the abundant amount of available online and offline text data, there
arises a crucial need to extract the relation between phrases and summarize the
main content of each document in a few words. For this purpose, there have been
many studies recently in Open Information Extraction (OIE). OIE improves upon
relation extraction techniques by analyzing relations across different domains
and avoids requiring hand-labeling pre-specified relations in sentences. This
paper surveys recent approaches of OIE and its applications on Knowledge Graph
(KG), text summarization, and Question Answering (QA). Moreover, the paper
describes OIE basis methods in relation extraction. It briefly discusses the
main approaches and the pros and cons of each method. Finally, it gives an
overview about challenges, open issues, and future work opportunities for OIE,
relation extraction, and OIE applications.Comment: 15 pages, 9 figure
Computer-aided biomimetics : semi-open relation extraction from scientific biological texts
Engineering inspired by biology – recently termed biom* – has led to various ground-breaking technological developments. Example areas of application include aerospace
engineering and robotics. However, biom* is not always successful and only sporadically applied in industry. The reason is that a systematic approach to biom* remains
at large, despite the existence of a plethora of methods and design tools. In recent
years computational tools have been proposed as well, which can potentially support
a systematic integration of relevant biological knowledge during biom*. However,
these so-called Computer-Aided Biom* (CAB) tools have not been able to fill all
the gaps in the biom* process. This thesis investigates why existing CAB tools
fail, proposes a novel approach – based on Information Extraction – and develops a
proof-of-concept for a CAB tool that does enable a systematic approach to biom*.
Key contributions include: 1) a disquisition of existing tools guides the selection of a strategy for systematic CAB, 2) a dataset of 1,500 manually-annotated
sentences, 3) a novel Information Extraction approach that combines the outputs
from a supervised Relation Extraction system and an existing Open Information
Extraction system. The implemented exploratory approach indicates that it is possible to extract a focused selection of relations from scientific texts with reasonable
accuracy, without imposing limitations on the types of information extracted. Furthermore, the tool developed in this thesis is shown to i) speed up a trade-off analysis
by domain-experts, and ii) also improve the access to biology information for non-exper
Computer-Aided Biomimetics : Semi-Open Relation Extraction from scientific biological texts
Engineering inspired by biology – recently termed biom* – has led to various groundbreaking technological developments. Example areas of application include aerospace
engineering and robotics. However, biom* is not always successful and only sporadically applied in industry. The reason is that a systematic approach to biom* remains
at large, despite the existence of a plethora of methods and design tools. In recent
years computational tools have been proposed as well, which can potentially support
a systematic integration of relevant biological knowledge during biom*. However,
these so-called Computer-Aided Biom* (CAB) tools have not been able to fill all
the gaps in the biom* process. This thesis investigates why existing CAB tools
fail, proposes a novel approach – based on Information Extraction – and develops a
proof-of-concept for a CAB tool that does enable a systematic approach to biom*.
Key contributions include: 1) a disquisition of existing tools guides the selection of a strategy for systematic CAB, 2) a dataset of 1,500 manually-annotated
sentences, 3) a novel Information Extraction approach that combines the outputs
from a supervised Relation Extraction system and an existing Open Information
Extraction system. The implemented exploratory approach indicates that it is possible to extract a focused selection of relations from scientific texts with reasonable
accuracy, without imposing limitations on the types of information extracted. Furthermore, the tool developed in this thesis is shown to i) speed up a trade-off analysis
by domain-experts, and ii) also improve the access to biology information for nonexperts