Article
Inference and Reconciliation in a Crowdsourced Lexical-Semantic Network
Fecha
2013-06-07Registro en:
Revista Computación y Sistemas; Vol. 17 No.2
1405-5546
Autor
Zarrouk, Manel
Lafourcade, Mathieu
Joubert, Alain
Institución
Resumen
Abstract. Lexical-semantic network construction and
validation is a major issue in NLP. No matter the
construction strategies used, automatically inferring new
relations from already existing ones is a way to improve
the global quality of the resource by densifying the
network. In this context, the purpose of an inference
engine is to formulate new conclusions (i.e. relations
between terms) from already existing premises (also
relations) on the network. In this paper we devise an
inference engine for the JeuxDeMots lexical network
which contains terms and typed relations between
terms. In the JeuxDeMots project, the lexical network
is constructed with the help of a game with a purpose
and thousands of players. Polysemous terms may be
refined in several senses (bank may be a bank-financial
institution or a bank-river) but as the network is
indefinitely under construction (in the context of a
Never Ending Learning approach) some senses may be
missing. The approach we propose is based on the
triangulation method implementing semantic transitivity
with a blocking mechanism for avoiding proposing
dubious new relations. Inferred relations are proposed
to contributors to be validated. In case of invalidation, a
reconciliation strategy is undertaken to identify the cause
of the wrong inference : an exception, an error in the
premises or a transitivity confusion due to polysemy with
the identification of the proper word senses at stake.