Existing computational methods for the analysis of corpora of text in natural
language are still far from approaching a human level of understanding. We
attempt to advance the state of the art by introducing a model and algorithmic
framework to transform text into recursively structured data. We apply this to
the analysis of news titles extracted from a social news aggregation website.
We show that a recursive ordered hypergraph is a sufficiently generic structure
to represent significant number of fundamental natural language constructs,
with advantages over conventional approaches such as semantic graphs. We
present a pipeline of transformations from the output of conventional NLP
algorithms to such hypergraphs, which we denote as semantic hypergraphs. The
features of these transformations include the creation of new concepts from
existing ones, the organisation of statements into regular structures of
predicates followed by an arbitrary number of entities and the ability to
represent statements about other statements. We demonstrate knowledge inference
from the hypergraph, identifying claims and expressions of conflicts, along
with their participating actors and topics. We show how this enables the
actor-centric summarization of conflicts, comparison of topics of claims
between actors and networks of conflicts between actors in the context of a
given topic. On the whole, we propose a hypergraphic knowledge representation
model that can be used to provide effective overviews of a large corpus of text
in natural language.
%0 Journal Article
%1 menezes2019semantic
%A Menezes, Telmo
%A Roth, Camille
%D 2019
%J CoRR
%K hypergraph nlp semantic semantics
%T Semantic Hypergraphs
%U http://arxiv.org/abs/1908.10784
%V abs/1908.10784
%X Existing computational methods for the analysis of corpora of text in natural
language are still far from approaching a human level of understanding. We
attempt to advance the state of the art by introducing a model and algorithmic
framework to transform text into recursively structured data. We apply this to
the analysis of news titles extracted from a social news aggregation website.
We show that a recursive ordered hypergraph is a sufficiently generic structure
to represent significant number of fundamental natural language constructs,
with advantages over conventional approaches such as semantic graphs. We
present a pipeline of transformations from the output of conventional NLP
algorithms to such hypergraphs, which we denote as semantic hypergraphs. The
features of these transformations include the creation of new concepts from
existing ones, the organisation of statements into regular structures of
predicates followed by an arbitrary number of entities and the ability to
represent statements about other statements. We demonstrate knowledge inference
from the hypergraph, identifying claims and expressions of conflicts, along
with their participating actors and topics. We show how this enables the
actor-centric summarization of conflicts, comparison of topics of claims
between actors and networks of conflicts between actors in the context of a
given topic. On the whole, we propose a hypergraphic knowledge representation
model that can be used to provide effective overviews of a large corpus of text
in natural language.
@article{menezes2019semantic,
abstract = {Existing computational methods for the analysis of corpora of text in natural
language are still far from approaching a human level of understanding. We
attempt to advance the state of the art by introducing a model and algorithmic
framework to transform text into recursively structured data. We apply this to
the analysis of news titles extracted from a social news aggregation website.
We show that a recursive ordered hypergraph is a sufficiently generic structure
to represent significant number of fundamental natural language constructs,
with advantages over conventional approaches such as semantic graphs. We
present a pipeline of transformations from the output of conventional NLP
algorithms to such hypergraphs, which we denote as semantic hypergraphs. The
features of these transformations include the creation of new concepts from
existing ones, the organisation of statements into regular structures of
predicates followed by an arbitrary number of entities and the ability to
represent statements about other statements. We demonstrate knowledge inference
from the hypergraph, identifying claims and expressions of conflicts, along
with their participating actors and topics. We show how this enables the
actor-centric summarization of conflicts, comparison of topics of claims
between actors and networks of conflicts between actors in the context of a
given topic. On the whole, we propose a hypergraphic knowledge representation
model that can be used to provide effective overviews of a large corpus of text
in natural language.},
added-at = {2020-08-06T08:36:50.000+0200},
author = {Menezes, Telmo and Roth, Camille},
biburl = {https://www.bibsonomy.org/bibtex/2343b5a9c86623717f5f57bc03ef510f6/jaeschke},
interhash = {7db8a97a4a8458a608202a5816c8748e},
intrahash = {343b5a9c86623717f5f57bc03ef510f6},
journal = {CoRR},
keywords = {hypergraph nlp semantic semantics},
timestamp = {2023-01-30T08:09:28.000+0100},
title = {Semantic Hypergraphs},
url = {http://arxiv.org/abs/1908.10784},
volume = {abs/1908.10784},
year = 2019
}