13,478 research outputs found
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
We present the architecture and the evaluation of a new system for
recognizing textual entailment (RTE). In RTE we want to identify automatically
the type of a logical relation between two input texts. In particular, we are
interested in proving the existence of an entailment between them. We conceive
our system as a modular environment allowing for a high-coverage syntactic and
semantic text analysis combined with logical inference. For the syntactic and
semantic analysis we combine a deep semantic analysis with a shallow one
supported by statistical models in order to increase the quality and the
accuracy of results. For RTE we use logical inference of first-order employing
model-theoretic techniques and automated reasoning tools. The inference is
supported with problem-relevant background knowledge extracted automatically
and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or
other, more experimental sources with, e.g., manually defined presupposition
resolutions, or with axiomatized general and common sense knowledge. The
results show that fine-grained and consistent knowledge coming from diverse
sources is a necessary condition determining the correctness and traceability
of results.Comment: 25 pages, 10 figure
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
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