Onto.PT: Towards the Automatic Construction of a Lexical Ontology ...

2.2. Lexical Knowledge Formalisms and Resources 17
1.3 Idioms 15
sea
climbing
mud
clay
ice
fire
gravel
sand
water
earth
peat
soil
air
rock
Figure 1.1: Graph snippet reflecting the ambiguity of the word rock. Therearetwodifferent
Figure meanings 2.7: Aof term-based rock represented lexical in thenetwork graph: rock with “stone” theand neighbours rock “music”. of the word rock in a
corpus (from Dorow (2006)). This word might refer to a stone or to a kind of music.
jazz
music
Semantic categories tend to aggregate in dense clusters in the word graph, such as the
“music” and the “natural material” clusters in Figure 1.1. These node clusters are held
together by ambiguous words such as rock which link otherwise unconnected word clusters.
We attempt to divide the word graph into cohesive semantic categories by identifying and
disabling these semantic “hubs”.
In addition, we investigate an alternative approach which divides the links in the graph
into clusters instead of the nodes. Thelinksinthewordgraphcontainmorespecificcontextual
information and are thus less ambiguous than the nodes which represent words in
isolation. For example the link (rock, gravel) clearlyaddressesthe“stone”senseofrock,
and the link between rock and jazz unambiguously refers to rock “music”. By dividing the
links of the word graph into clusters, links pertaining to the samesenseofawordcan
be grouped together (e.g. (rock, gravel) and(rock, sand)), and links which correspond to
different senses of a word (e.g. (rock, gravel) and(rock, jazz)) can be assigned to different
clusters, which means that an ambiguous word is naturally split up into its different senses.
relations, the nodes can be seen as word senses. In this case, an edge E(wi, wj)
indicates that one sense of wi is related to one sense of wj.
PAPEL (Gonçalo Oliveira et al., 2008, 2010b) and CARTÃO (Gonçalo Oliveira
et al., 2011) are resources that may be used as term-based lexical networks for
Portuguese. They are structured in relational triples automatically extracted from
dictionary definitions. Each triple t = {w1, R, w2} represents a semantic relation
identified by R, which occurs between a sense of the lexical item w1 and a sense of
lexical item w2. A lexical network is established if the arguments of the relational
triples, w1 and w2, are used as nodes, connected by an edge labelled as R, the type
of the relation. Figure 2.8 shows part of a term-based lexical network with relations.
1.3 Idioms
Idioms pose an even greater challenge to systems trying to understand human language
than ambiguity. Idioms are used extensively both in spoken and written, colloquial and formal
language. Any successful natural language processing system must be able to recognize
and interpret idiomatic expressions.
Figure 2.8: A term-based lexical network with relations where banco is one of the
arguments (from PAPEL 2.0 (Gonçalo Oliveira et al., 2010b)). In Portuguese, besides
other meanings, banco might refer to a bench/stool or to a financial institution.
pop
soul
art
film