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Objective Hierarchy of Abstract Concepts : Organization of Abstract Nouns via Distribution of Adjectives

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Authors

Kanzaki, Kyoko; Ma, Qing; Yamamoto, Eiko; Murata, Masaki; Isahara, Hitoshi

Issue Date
2003
Publisher
Institute for Cognitive Science, Seoul National University
Citation
Journal of cognitive science, Vol.4 No.2, pp. 201-225
Abstract
Our purpose in this research is to find hypernimic concepts of words
experimentally by using a large corpora and a neural network model. At first
we treat adjectives. We focused on semantic relations between abstract nouns
and adjectives. We made linguistic data by extracting semantic relations
between abstract nouns and adjectives from large corpora, and we use them as
an input data for the Self-Organizing Semantic Map (SOM), which is a neural
network model (Kohonen 1995). On the Semantic Map, words are located near
to or far from each other depending on their similarities. In some previous research, word meanings were classified from linguistic data based on
syntactic information using a statistical method. Hindle (1990) used syntactic
relations between nouns and verbs, and Hatzivassiloglou and McKeown
(1993) used semantic relations between adjective-adjective pairs, both of
which are modifiers for a head noun. Their methods are useful for an
organization of words in a subordinate layer, however, if we consider it in a
superordinate layer, their methods seem not to be enough to solve the problem.
Previous researches on the classification of words by a SOM treat a small
amount of data (Kohonen et al., 1995), however, we made an experiment with
a large amount of data, such as 42 years worth of newspaper articles.
ISSN
1598-2327
Language
English
URI
https://hdl.handle.net/10371/70733
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