id: 06071815
dt: a
an: 06071815
au: Paukkeri, Mari-Sanna; Väyrynen, Jaakko; Arppe, Antti
ti: Exploring extensive linguistic feature sets in near-synonym lexical choice.
so: Gelbukh, Alexander (ed.), Computational linguistics and intelligent text
    processing. 13th international conference, CICLing 2012, New Delhi,
    India, March 11‒17, 2012. Proceedings, Part II. Berlin: Springer
    (ISBN 978-3-642-28600-1/pbk). Lecture Notes in Computer Science 7182,
    1-12 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: near-synonym lexical choice; linguistic features
ci: 
li: doi:10.1007/978-3-642-28601-8_1
ab: Summary: In the near-synonym lexical choice task, the best alternative out
    of a set of near-synonyms is selected to fill a lexical gap in a text.
    We experiment on an approach of an extensive set, over 650, linguistic
    features to represent the context of a word, and a range of machine
    learning approaches in the lexical choice task. We extend previous work
    by experimenting with unsupervised and semi-supervised methods, and use
    automatic feature selection to cope with the problems arising from the
    rich feature set. It is natural to think that linguistic analysis of
    the word context would yield almost perfect performance in the task but
    we show that too many features, even linguistic, introduce noise and
    make the task difficult for unsupervised and semi-supervised methods.
    We also show that purely syntactic features play the biggest role in
    the performance, but also certain semantic and morphological features
    are needed.
rv: