The document discusses techniques for discriminating between different meanings (senses) of words based on their usage context. It presents a methodology that clusters similar contexts of a target word based on lexical features. Contexts are represented as vectors, and similarities are measured to group contexts and label clusters. Experimental results show second-order representations that capture indirect relationships generally perform better, while first-order may be better for larger, more homogeneous data. Software tools described implement various natural language processing and word sense discrimination techniques.

1. Name Discrimination by Clustering Similar Contexts Ted Pedersen & Anagha Kulkarni University of Minnesota, Duluth Amruta Purandare Now at University of Pittsburgh Research Supported by National Science Foundation Faculty Early Career Development Award (#0092784)

16. 2 nd Order Context Vectors Context sea shore store

18. Limitations of 1 st or 2 nd order 0 52.27 0 0.92 0 4.21 0 28.72 0 3.24 0 1.28 0 2.53 Weapon Missile Shoot Fire Destroy Murder Kill 17.77 0 14.6 46.2 22.1 0 34.2 19.23 2.36 0 72.7 0 1.28 2.56 Execute Command Bomb Pipe Fire CD Burn

22. Evaluation (before mapping) c1 c2 c4 c3 2 1 15 2 C4 6 1 1 2 C3 1 7 1 1 C2 2 3 0 10 C1

23. Evaluation (after mapping) Accuracy=38/55=0.69 20 15 2 1 2 C4 17 1 1 0 55 11 12 15 10 6 1 2 C3 10 1 7 1 C2 15 2 3 10 C1

24. Majority Sense Classifier Maj. =17/55=0.31

27. Experimental Conclusions 2 nd order, RB Smaller Data (like SENSEVAL-2) 1 st order, UPGMA Large, Homogeneous (like Line, Hard, Serve) Recommendation Nature of Data