1. NUMBER OR NUANCE: Factors Affecting Reliable Word Sense Annotation Susan Windisch Brown, Travis Rood, and Martha Palmer University of Colorado at Boulder

2. Annotators in their little nests agree; And ‘tis a shameful sight, When taggers on one project Fall out, and chide, and fight. —[adapted from] Isaac Watts

3. Automatic word sense disambiguation  Lexical ambiguity is a significant problem in natural language processing (NLP) applications (Agirre & Edmonds, 2006)  Text summarization  Question answering  WSD systems might help  Several studies show benefits for NLP tasks (Sanderson, 2000; Stokoe, 2003; Carpuat and Wu, 2007; Chan, Ng and Chiang, 2007)  But only with higher system accuracy (90%+) 3

4. Annotation reliability affects system accuracy 4 WSD systemSystem Performance Inter- annotator agreement Sense Inventory SensEval262.5%70%WordNet Chen et al. (2007) 82%89%OntoNotes Palmer (2008) 90%94%PropBank

5. Senses for the verb control 5 WordNetOntoNotes 1. exercise authoritative control or power over 1. exercise power or influence over; hold within limits 2. control (others or oneself) or influence skillfully 3. handle and cause to function 4. lessen the intensity of; temper 5. check or regulate (a scientific experiment) by conducting a parallel experiment 2. verify something by comparing to a standard 6. verify by using a duplicate register for comparison 7. be careful or certain to do something 8. have a firm understanding of

6. Possible factors affecting the reliability of word sense annotation 6  Fine-grained senses result in many senses per word, creating a heavy cognitive load on annotators, making accurate and consistent tagging difficult  Fine-grained senses are not distinct enough to reliably discriminate between

7. Requirements to compare fine-grained and coarse-grained annotation 7  Annotation of the same words on the same corpus instances  Sense inventories differing only in sense granularity  Previous work (Ng et al., 1999; Edmonds & Cotton, 2001; Navigli et al. 2007)

8. 3 experiments 8  40 verbs  Number of senses : 2-26  Sense granularity: WordNet vs. OntoNotes  Exp. 1: confirm difference in reliability between fine- and coarse-grained annotation; vary granularity and number of senses  Exp. 2: hold granularity constant; vary number of senses  Exp. 3: hold number constant; vary granularity

9. Experiment 1  Compare fine-grained sense inventory to coarse  70 instances for each verb from the ON corpus  Annotated with WN senses by multiple pairs of annotators  Annotated with ON senses by multiple pairs of annotators  Compare the ON ITAs to the WN ITAs 9 Ave. number of senses Granularity OntoNotes6.2Coarse WN14.6Fine

10. Results 10

11. Results  Coarse-grained ON annotations had higher ITAs than fine-grained WN annotations  Number of senses No significant effect (t (79) = -1.28, p =.206).  Sense nuance Yes, a significant effect (t (79) = 10.39, p <.0001). With number of senses held constant, coarse-grained annotation is 16.2 percentage points higher than fine- grained. 11

12. Experiment 2: Number of senses  Hold sense granularity constant; vary # of senses  2 pairs of annotators, using fine-grained WN senses  First pair uses full set of WN senses for a word  Second pair uses a restricted set on instances that we know should fit one of those senses 12 Ave. number of sensesGranularity WN Full set14.6Fine WN Restricted set5.6Fine

13. 13 OntoNotes grouped sense B OntoNotes grouped sense C OntoNotes grouped sense A WN 3 7 8 13 14 WN 9 10 WN 1 2 4 5 6 11 12

14. "Then I just bought plywood, drew the pieces on it and cut them out."  1. ----------------  2. ----------------  3. ----------------  4. ----------------  5. ----------------  6. ----------------  7. ----------------  8. ----------------  9. ----------------  10. ----------------  11. ----------------  12. ----------------  13. ----------------  14. ----------------  3. ----------------  7. ----------------  8. ----------------  13. ----------------  14. ---------------- 14 Full set of WN sensesRestricted set of WN senses

15. Results 15

16. Experiment 3  Number of senses controlled; vary sense granularity  Compare the ITAs for the ON tagging with the restricted-set WN tagging 16 Ave. number of senses Granularity OntoNotes6.2Coarse WN Restricted set5.6Fine

17. Results 17

18. Conclusion  Number of senses annotators must choose between: never a significant factor  Granularity of the senses: a significant factor, with fine-grained senses leading to lower ITAs  Poor reliability of fine-grained word sense annotation cannot be improved by reducing the cognitive load on annotators.  Annotators cannot reliably discriminate between nuanced sense distinctions. 18

19. Acknowledgements 19 We gratefully acknowledge the efforts of all of the annotators and the support of the National Science Foundation Grants NSF-0415923, Word Sense Disambiguation and CISE-CRI-0551615, Towards a Comprehensive Linguistic Annotation and CISE-CRI 0709167, as well as a grant from the Defense Advanced Research Projects Agency (DARPA/IPTO) under the GALE program, DARPA/CMO Contract No. HR0011-06-C-0022, a subcontract from BBN, Inc.

20. Restricted set annotation 20  Use the adjudicated ON data to determine the ON sense for each instance.  Use instances from experiment1 that were labeled with one selected ON sense (35 instances).  Each restricted-set annotator saw only the WN senses that were clustered to form the appropriate ON sense.  Compare to the full set annotation for those instances.