We address the problem of unknown word sense detection: the identification of corpus occurrences that are not covered by a given sense inventory. We model this as an instance of outlier detection, using a simple nearest neighbor-based approach to measuring the resemblance of a new item to a training set. In combination with a method that alleviates data sparseness by sharing training data across lemmas, the approach achieves a precision of 0.77 and recall of 0.82.
One of the first attempts in this area was made by Erk REF , where the author tried to model this problem as an instance of outlier detection, using a simple nearest neighbor-based approach.
Unknown Word Sense Detection As Outlier Detection
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science