Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Using WordNet to automatically deduce relations between words in noun-noun compounds
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On the semantics of noun compounds
Computer Speech and Language
Automatic interpretation of noun compounds using wordnet similarity
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Domain and function: a dual-space model of semantic relations and compositions
Journal of Artificial Intelligence Research
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This study examines the ability of a semantic space model to represent themeaning of noun compounds such as "information gathering" or "weather forecast". A new algorithm, comparison, is proposed for computing compound vectors from constituent word vectors, and compared with other algorithms (i.e., predication and centroid) in terms of accuracy of multiple-choice synonym test and similarity judgment test. The result of both tests is that the comparison algorithm is, on the whole, superior to other algorithms, and in particular achieves the best performance when noun compounds have emergent meanings. Furthermore, the comparison algorithm also works for novel noun compounds that do not occur in the corpus. These findings indicate that a semantic space model in general and the comparison algorithm in particular has sufficient ability to compute the meaning of noun compounds.