Hedging

Brennan, S. E., & Ohaeri, J. O. (1999). Why do electronic conversations seem less polite? the costs and benefits of hedging. ACM SIGSOFT Software Engineering Notes, 24(2), 227Ð235. doi:10.1145/295666.295942. (PDF)

Brooke, M. E., & Ng, S. H. (1986). Language and social influence in small conversational groups. Journal of Language and Social Psychology, 5(3), 201Ð210. doi:10.1177/0261927x8600500303 (PDF)

Farkas, R., Vincze, V., Mora, G., Csirik, J., & Szarvas, G. (2010). CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning: Shared Task (pp. 1Ð12). (PDF)

Gries, S., & David, C. (2007). This is kind of/sort of interesting: variation in hedging in English. Towards Multimedia in Corpus Studies, 1Ð17. Retrieved from http://www.linguistics.ucsb.edu/faculty/stgries/research/2007_STG-CVD_KindOfSortOf_MultMedCorpLing.pdf (PDF)

Hinkel, E. (n.d.). Hedges and Intensifiers in Written Discourse in non-Anglo-American Rhetorical Traditions. Applied Language Learning, 15. (PDF)

Kang, Sin-Jae, In-Su Kang, and Seung-Hoon Na. (2011). A Comparison of Classifiers for Detecting Hedges. U- and E-Service, Science and Technology. Springer Berlin Heidelberg, 251-257. (PDF)

Prince, E., Bosk, C. & Frader, J. (1982). On hedging in physician-physician discourse. In Linguistics and the Professions, Robert di Pietro (ed.), 83Ð97. Norwood/New Jersey: Ablex. (PDF)

Wei, Z., Chen, J., Gao, W., Li, B., Zhou, L., He, Y., & Wong, K-F. (2013). An empirical study on uncertainty identification in social media context. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 58Ð62, Sofia, Bulgaria, August. (PDF)



CrowdSourcing

Akkaya, C., Conrad, A., Wiebe, J., & Mihalcea, R. (2010). Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation. Computational Linguistics, (June), 195Ð203. Retrieved from http://www.aclweb.org/anthology/W10-0731 (PDF)

Biemann, C., & Nygaard, V. (2010). Crowdsourcing WordNet. Proceedings of the 5th Global WordNet Conference Mumbai India ACL Data and Code Repository ADCR2010T005, 5659Ð5664. Retrieved from http://www.cfilt.iitb.ac.in/gwc2010/pdfs/04_Crowdsourcing_WordNet__Biemann.pdf (PDF).

Scott, D., Barone, R., & Koeling, R. (2012). Corpus annotation as a scientific task, 1481Ð1485. Retrieved from http://www.lrec-conf.org/proceedings/lrec2012/index.html (PDF)

Snow, R., OÕConnor, B., Jurafsky, D., & Ng, a. Y. (2008). Cheap and fastÑbut is it good?: evaluating non-expert annotations for natural language tasks. Proceedings of the Conference on Empirical Methods in Natural Language Processing, (October), 254Ð263. doi:10.1.1.142.8286 (PDF)

Tang, W., & Lease, M. (2011). Semi-supervised consensus labeling for crowdsourcing. SIGIR 2011 Workshop on Crowdsourcing for É, 36Ð41. Retrieved from https://www.ischool.utexas.edu/~ml/papers/tang-cir11.pdf (PDF)



Active Learning

Chen, J., Schein, A., Ungar, L., & Palmer, M. (2006). An Empirical Study of the Behavior of Active Learning for Word Sense Disambiguation. In HLT Conference of the North American ACL (pp. 120Ð127). (PDF)

Chklovski, T., & Mihalcea, R. (2002). Building a Sense Tagged Corpus with Open Mind Word Expert. Proceedings of the SIGLEX/SENSEVAL Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, 116Ð122. doi:10.3115/1118675.1118692 :(PDF)

Laws, F., Scheible, C., & SchŸtze, H. (2011). Active Learning with Amazon Mechanical Turk. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 1546Ð1556. (PDF)

Sheng, V. S., & Provost, F. (2008). Get Another Label? Improving Data Quality and Data Mining Using Multiple , Noisy Labelers Categories and Subject Descriptors, 614Ð622. (PDF)

Yan, Y., Rosales, R., Fung, G., & Dy, J. G. (2011). Active Learning from Crowds. In Proceedings of the 28th International Conference on Machine Learning. Bellevue, WA, USA. (PDF)

Zhu, J., Hovy, E., & Rey, M. (2007). Active Learning for Word Sense Disambiguation with Methods for Addressing the Class Imbalance Problem. Computational Linguistics, (June), 783Ð790. (PDF)

Zhu, J., Wang, H., Yao, T., & Tsou, B. K. (2008). Active Learning with Sampling by Uncertainty and Density for Word Sense Disambiguation and Text Classification. Proceedings of the 22nd International Conference on Computational Linguistics, (August), 1137Ð1144. doi:10.1109/TASL.2009.2033421 (PDF)



Belief, Uncertainty and Speculative Language

Krestel, R., Witte, R., & Bergler, S. (2007). Processing of Beliefs Extracted from Reported Speech in Newspaper Articles. Proceedings of RANLP 2007, 27Ð29. (PDF)

Prabhakaran, Vinodkumar, Owen Rambow, and Mona Diab. "Automatic committed belief tagging." In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 1014-1022. Association for Computational Linguistics, 2010. (PDF)

Rubin, V. L. (2007). Stating with Certainty or Stating with Doubt: Intercoder Reliability Results for Manual Annotation of Epistemically Modalized Statements. Computational Linguistics, (April), 141Ð144. (PDF)

Rubin, V. L., Kando, N., & Liddy, E. D. (2004). Certainty Categorization Model. New York. (PDF)

Ruppenhofer, J., & Rehbein, I. (2010). Yes we can?!? Annotating the senses of English modal verbs. Eighth International Conference on Language Resources and Evaluation (LREC-2012), 1538Ð1545. (PDF)

Sauri, R., & Pustejovsky, J. (2007). Determining modality and factuality for text entailment. ICSC 2007 International Conference on Semantic Computing, (3), 509Ð516. doi:10.1109/ICSC.2007.80 (PDF)

Sauri, R., Verhagen, M., & Pustejovsky, J. (2004). Annotating and Recognizing Event Modality in Text. Human Rights, 333Ð338. (PDF)

Wilson, T., & Wiebe, J. (2005). Annotating attributions and private states. Ann Arbor, 100(June), 53Ð60. doi:10.3115/1608829.1608837 (PDF)