9 research outputs found
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
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
Transforming Complex Sentences into a Semantic Hierarchy
We present an approach for recursively splitting and rephrasing complex
English sentences into a novel semantic hierarchy of simplified sentences, with
each of them presenting a more regular structure that may facilitate a wide
variety of artificial intelligence tasks, such as machine translation (MT) or
information extraction (IE). Using a set of hand-crafted transformation rules,
input sentences are recursively transformed into a two-layered hierarchical
representation in the form of core sentences and accompanying contexts that are
linked via rhetorical relations. In this way, the semantic relationship of the
decomposed constituents is preserved in the output, maintaining its
interpretability for downstream applications. Both a thorough manual analysis
and automatic evaluation across three datasets from two different domains
demonstrate that the proposed syntactic simplification approach outperforms the
state of the art in structural text simplification. Moreover, an extrinsic
evaluation shows that when applying our framework as a preprocessing step the
performance of state-of-the-art Open IE systems can be improved by up to 346%
in precision and 52% in recall. To enable reproducible research, all code is
provided online