1,072 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
Answering Complex Questions Using Open Information Extraction
While there has been substantial progress in factoid question-answering (QA),
answering complex questions remains challenging, typically requiring both a
large body of knowledge and inference techniques. Open Information Extraction
(Open IE) provides a way to generate semi-structured knowledge for QA, but to
date such knowledge has only been used to answer simple questions with
retrieval-based methods. We overcome this limitation by presenting a method for
reasoning with Open IE knowledge, allowing more complex questions to be
handled. Using a recently proposed support graph optimization framework for QA,
we develop a new inference model for Open IE, in particular one that can work
effectively with multiple short facts, noise, and the relational structure of
tuples. Our model significantly outperforms a state-of-the-art structured
solver on complex questions of varying difficulty, while also removing the
reliance on manually curated knowledge.Comment: Accepted as short paper at ACL 201
Graphene: A Context-Preserving Open Information Extraction System
We introduce Graphene, an Open IE system whose goal is to generate accurate,
meaningful and complete propositions that may facilitate a variety of
downstream semantic applications. For this purpose, we transform syntactically
complex input sentences into clean, compact structures in the form of core
facts and accompanying contexts, while identifying the rhetorical relations
that hold between them in order to maintain their semantic relationship. In
that way, we preserve the context of the relational tuples extracted from a
source sentence, generating a novel lightweight semantic representation for
Open IE that enhances the expressiveness of the extracted propositions.Comment: 27th International Conference on Computational Linguistics (COLING
2018
Graphene: Semantically-Linked Propositions in Open Information Extraction
We present an Open Information Extraction (IE) approach that uses a
two-layered transformation stage consisting of a clausal disembedding layer and
a phrasal disembedding layer, together with rhetorical relation identification.
In that way, we convert sentences that present a complex linguistic structure
into simplified, syntactically sound sentences, from which we can extract
propositions that are represented in a two-layered hierarchy in the form of
core relational tuples and accompanying contextual information which are
semantically linked via rhetorical relations. In a comparative evaluation, we
demonstrate that our reference implementation Graphene outperforms
state-of-the-art Open IE systems in the construction of correct n-ary
predicate-argument structures. Moreover, we show that existing Open IE
approaches can benefit from the transformation process of our framework.Comment: 27th International Conference on Computational Linguistics (COLING
2018
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