TY - GEN
T1 - Using latent semantic indexing as a measure of conceptual association for noun compound disambiguation
AU - Buckeridge, Alan M.
AU - Sutcliffe, Richard F.E.
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
PY - 2002
Y1 - 2002
N2 - Noun compounds are a frequently occurring yet highly ambiguous construction in natural language; their interpretation relies on extra-syntactic information. Several statistical methods for compound disambiguation have been reported in the literature; however, a striking feature of all these approaches is that disambiguation relies on statistics derived from unambiguous compounds in training, meaning they are prone to the problem of sparse data. Other researchers have overcome this difficulty somewhat by using manually crafted knowledge resources to collect statistics on "concepts" rather than noun tokens, but have sacrificed domain-independence by doing so. We report here on work investigating the application of Latent Semantic Indexing [4], an Information Retrieval technique, to the task of noun compound disambiguation. We achieved an accuracy of 84%, indicating the potential of applying vector-based distributional information measures to syntactic disambiguation.
AB - Noun compounds are a frequently occurring yet highly ambiguous construction in natural language; their interpretation relies on extra-syntactic information. Several statistical methods for compound disambiguation have been reported in the literature; however, a striking feature of all these approaches is that disambiguation relies on statistics derived from unambiguous compounds in training, meaning they are prone to the problem of sparse data. Other researchers have overcome this difficulty somewhat by using manually crafted knowledge resources to collect statistics on "concepts" rather than noun tokens, but have sacrificed domain-independence by doing so. We report here on work investigating the application of Latent Semantic Indexing [4], an Information Retrieval technique, to the task of noun compound disambiguation. We achieved an accuracy of 84%, indicating the potential of applying vector-based distributional information measures to syntactic disambiguation.
UR - http://www.scopus.com/inward/record.url?scp=84937410998&partnerID=8YFLogxK
U2 - 10.1007/3-540-45750-x_2
DO - 10.1007/3-540-45750-x_2
M3 - Conference contribution
AN - SCOPUS:84937410998
T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
SP - 12
EP - 19
BT - Artificial Intelligence and Cognitive Science - 13th Irish Conference, AICS 2002, Proceedings
A2 - O’Neill, Michael
A2 - Sutcliffe, Richard F. E.
A2 - Ryan, Conor
A2 - Eaton, Malachy
A2 - Griffith, Niall J. L.
PB - Springer Verlag
T2 - 13th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2002
Y2 - 12 September 2002 through 13 September 2002
ER -