Ted Pedersen is a professor of computer science at the University of Minnesota Duluth who does research in natural language processing. His lab has developed several unsupervised word sense discrimination techniques based on clustering similar contexts without requiring sense-tagged training data. This approach uses local lexical features and measures similarities between contexts to cluster words into senses. Evaluation on Senseval-2 data showed this approach outperformed a majority sense classifier baseline.

1. Unsupervised Word Sense Discrimination By Clustering Similar Contexts Ted Pedersen University of Minnesota, Duluth http:// www.d.umn.edu/~tpederse Research Supported by National Science Foundation Faculty Early Career Development Award (#0092784)

3. NLP @ UMD, Fall 2004

5. Alumni

9. Unsupervised Word Sense Discrimination By Clustering Similar Contexts With Considerable Assistance From Anagha Kulkarni (M.S. 2006) Amruta Purandare (M.S. 2004)

10. Overview shells exploded in a US diplomatic complex in Liberia shell scripts are user interactive artillery guns were used to fire highly explosive shells the biggest shop on the shore for serious shell collectors shell script is a series of commands written into a file that Unix executes she sells sea shells by the sea shore sherry enjoys walking along the beach and collecting shells firework shells exploded onto usually dark screens in a variety of colors shells automate system administrative tasks we specialize in low priced corals, starfish and shells we help people in identifying wonderful sea shells along the coastlines shop at the biggest shell store by the shore shell script is much like the ms dos batch file

11. sherry enjoys walking along the beach and collecting shells we specialize in low priced corals, starfish and shells we help people in identifying wonderful sea shells along the coastlines shop at the biggest shell store by the shore she sells sea shells by the sea shore the biggest shop on the shore for serious shell collectors shell script is much like the ms dos batch file shell script is a series of commands written into a file that Unix executes shell scripts are user interactive shells automate system administrative tasks shells exploded in a US diplomatic complex in Liberia firework shells exploded onto usually dark screens in a variety of colors artillery guns were used to fire highly explosive shells

27. 2 nd Order Context Vectors Context sea shore store

29. Limitations 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

32. 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

33. 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

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

37. Experiment 1: Results POS wise 29 NOUNS 28verbs 15 adjs No of words of a POS for which experiment obtained accuracy more than Majority 8 7 3 5 7 6 MAT COS SOC UNI BI MAT COS MAT COS 0 1 0 0 1 1 9 13 5 5 6 11 SOC UNI BI SOC UNI BI

38. Experiment 1: Results Feature wise SOC BI UNI 32 18 38 1 1 6 11 7 6 MAT COS N ADJ V MAT COS MAT COS 0 1 9 13 8 7 0 0 5 5 3 5 N ADJ V N ADJ V

39. Experiment 1: Results Measure wise COS MAT 49 39 0 5 5 1 1 13 11 7 6 BI UNI SOC N ADJ V BI UNI SOC 0 5 3 0 1 9 6 8 7 N ADJ V

42. Experiment 2: Sval2 Results Bi-grams Vs Co-occurrences Bi-gram = COC 0 2 1 Bi-gram < COC 1 1 4 Bi-gram > COC 3 3 9 Bi-gram = COC 0 1 1 Bi-gram < COC 2 4 6 Bi-gram > COC 2 1 7 V A N PB1 Vs PB3 SC1 Vs SC3

43. Experiment 2: Sval2 Results RB Vs UPGMA RB = UPGMA 1 0 4 RB < UPGMA 0 5 2 RB > UPGMA 3 1 8 RB = UPGMA 1 2 1 RB < UPGMA 2 0 4 RB > UPGMA 1 4 9 V A N PB1 Vs PB2 SC1 Vs SC2

44. Experiment 2: Sval2 Results Comparing with MAJ 5 2 0 3 PB3 > MAJ 6 1 1 4 PB1 > MAJ 9 2 1 6 SC2 > MAJ 9 0 2 7 PB2 > MAJ 10 2 2 6 SC1 > MAJ 12 1 3 8 SC3 > MAJ Total V A N

45. Experiment 2: Results Line, Hard, Serve (TOP 3) PB2 PB1 PB3 Serve.v SC2 PB1 PB3 Hard.a PB2 PB3 PB1 Line.n 3 rd 2 nd 1 st

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

55. Hi, from Duluth!