This study compares simple hash-based methods for computing distributional similarity with the complex singular value decomposition approach. It comes to the conclusion that the simpler methods produce both better quality and can be computed at a fraction of the time required for SVD. (2011)
This work presents an algorithm that is able to detect verbs relying on completely unsupervised language processing methods. Being able to recognize action clues in textual resources enables our applications to apply methods for deeper language understanding (such as relation extraction) in a completely unsupervised manner.
Hänig, C.: Knowledge-free Verb Detection through Tag Sequence Alignment. In: Proceedings of the 18th International Nordic Conference of Computational Linguistics (NODALIDA 2011), Riga, Latvia, Northern European Association for Language Technology (NEALT), 2011
Our approach to sentiment analysis shows that polarity of phrases can be composed out of the word’s polarity. Our polarity model is language-indepedent and thus, can be easily adapted to new languages / domains.
Robert Remus und Christian Hänig: Towards Well-grounded Phrase-level Polarity Analysis. In: Proceedings of the 12th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing) , Springer, 2011