# Annotating Named Entities in Twitter Data with Crowdsourcing

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5 Figure 4: Only about one-third of the workers did more than three HITs and a a few prolific workers accounted for most of our data. checkboxes. The form elements have automatically generated names, which MTurk handles neatly. Additional Javascript code collects location information from the workers, based on their IP address. A service provided by Geobytes 3 provides the location data. 4 Results from MTurk Our dataset was broken into HITs of four previously unlabeled tweets, and one previously labeled tweet (analogous to the gold data used by Crowd- Flower). We submitted 251 HITs, each of which was to be completed twice, and the job took about 15 hours. Total cost for this job was \$27.61, for a total cost per tweet of about 2.75 cents each (although we also paid to have the gold tweets annotated again). 42 workers participated, mostly from the US and India, with Australia in a distant third place. Most workers did only a single HIT, but most HITs were done by a single worker. Figure 4 shows more detail. After collecting results from MTurk, we had to come up with a strategy for determining which of the results (if any) were filled randomly. To do this, we implemented an algorithm much like Google s PageRank (Brin and Page, 1998) to judge the amount of inter-worker agreement. Pseudocode for our algorithm is presented in Figure 5. This algorithm doesn t strictly measure worker quality, but rather worker agreement, so it s impor- 3 WORKER-AGREE : results scores 1 worker ids ENUMERATE(KEYS(results)) Initialize A 2 for worker1 worker ids 3 do for worker2 worker ids 4 do A[worker1, worker2 ] SIMILARITY(results[worker1 ], results[worker2 ]) Normalize columns of A so that they sum to 1 (elided) Initialize x to be normal: each worker is initially trusted equally. 1 5 x 1 n,..., n Find the largest eigenvector of A, which corresponds to the agreement-with-group value for each worker. 6 i 0 7 while i < max iter 8 do xnew NORMALIZE(A x) 9 diff xnew x 10 x = xnew 11 if diff < tolerance 12 then break 13 i i for workerid, workernum worker ids 15 do scores[workerid] x[workernum] 16 return scores Figure 5: Intra-worker agreement algorithm. MTurk results are stored in an associative array, with worker IDs as keys and lists of HIT results as values, and worker scores are floating point values. Worker IDs are mapped to integers to allow standard matrix notation. The Similarity function in line four just returns the fraction of HITs done by two workers where their annotations agreed. tant to ensure that the workers it judges as having high agreement values are actually making highquality judgements. Figure 6 shows the worker agreement values plotted against the number of results a particular worker completed. The slope of this plot (more results returned tends to give higher scores) is interpreted to be because practice makes perfect: the more HITs a worker completes, the more experience they have with the task, and the more accurate their results will be. So, with this agreement metric established, we set out to find out how well it agreed with our expectation that it would also function as a quality metric. Consider those workers that completed only a single HIT (there are 18 of them): how well did they do their jobs, and where did they end up ranked as a result? Since each HIT is composed of five tweets, 84

9 mediate feedback to workers who enter a wrong answer for the gold standard queries, however. With these labeled tweets, we plan to train an entity recognizer using the Stanford named entity recognizer 4, and run it on our dataset. After using this trained entity recognizer to find the entities in our data, we will compare its accuracy to the existing recognized entities, which were recognized by an ER trained on newswire articles. We will also attempt to do named entity linking and entity resolution on the entire corpus. We look forward to making use of the data we collected in our research and expect that we will use these services in the future when we need human judgements. Acknowledgments This work was done with partial support from the Office of Naval Research and the Johns Hopkins University Human Language Technology Center of Excellence. We thank both Amazon and Dolores Labs for grants that allowed us to use their systems for the experiments. References S. Bird, E. Klein, and E. Loper Natural language processing with Python. Oreilly & Associates Inc. S. Brin and L. Page The anatomy of a large-scale hypertextual web search engine. In Seventh International World-Wide Web Conference (WWW 1998). C. Callison-Burch Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazons Mechanical Turk. In Proceedings of EMNLP N. Chinchor and P. Robinson MUC-7 named entity task definition. In Proceedings of the 7th Message Understanding Conference. NIST. crowdflower.com The error rates without the gold standard is more than twice as high as when we do use a gold standard. Accessed on April 11, J.R. Finkel, T. Grenager, and C. Manning Incorporating non-local information into information extraction systems by gibbs sampling. In Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics (ACL 2005), volume 100, pages A. Java, X. Song, T. Finin, and B. Tseng Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages ACM. Linguistic Data Consortium LCTL Team Simple named entity guidelines for less commonly taught languages, March. Version 6.5. pingdom.com Twitter: Now more than 1 billion tweets per month. /2010/02/10/twitter-now-more-than-1-billion-tweetsper-month/, February. Accessed on February 15, T. Poibeau and L. Kosseim Proper Name Extraction from Non-Journalistic Texts. In Computational linguistics in the Netherlands 2000: selected papers from the eleventh CLIN Meeting, page 144. Rodopi. J. Rocchio Relevance feedback in information retrieval. In G. Salton, editor, The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice-Hall. R. Snow, B. O Connor, D. Jurafsky, and A.Y. Ng Cheap and fast but is it good?: evaluating non-expert annotations for natural language tasks. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages Association for Computational Linguistics. Stephanie Strassel, Mark Przybocki, Kay Peterson, Zhiyi Song, and Kazuaki Maeda Linguistic resources and evaluation techniques for evaluation of cross-document automatic content extraction. In Proceedings of the 6th International Conference on Language Resources and Evaluation

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