Crowdsourcing in Natural Language Processing and Information Retrieval Sample Clauses

Crowdsourcing in Natural Language Processing and Information Retrieval. Amazon's Mechanical Turk (AMT) is an increasingly popular online crowdsourcing system. It provides a platform for collecting human intelligence for tasks that cannot be done by computers. There are two groups of people: requesters and workers. Requesters are people who submit tasks, and workers (also known as providers) are those who complete the tasks and receive monetary payments from the requesters. A task is called a HIT (Human Intelligence Task), which is usually in the form of one or more web pages containing text, images, or videos, and input elements like text boxes and radio buttons. Requesters can collect results from MTurk once the tasks are finished. As a cheap and fast service, MTurk is now being used by an increasingly large number of people for a variety of tasks. Mechanical Turk has been used for various natural language processing and information retrieval tasks. Xxxx et al. (2008) explored the use of MTurk for collecting annotations for five tasks: affect recognition, word similarity, recognising textual entailment, event temporal ordering and word sense disambiguation. They showed that across the tasks the use of non- expert labellers rivalled the performance of using an expert annotator. XXxxx has also been used for gathering relevance judgements for evaluating search output (Xxxxxx et al., 2008; Xxxxxx & Xxxxxxx, 2009; Xxxxxx & Xxxxx-Xxxxx, 2011; Xxxxxxxx et al., 2011; Xxxxxxxx et al., 2012; Xxxxx, 2011), generating data to assess attribute extraction and entity resolution (Su et al., 2007), image annotation (Xxxxxxx & Xxxxxxx, 2008), the assessment of the quality of Wikipedia articles (Xxxxxx et al., 2008), the extraction of document facets (Xxxxx & Ipeirotis, 2008), comparing different length summaries from search results (Xxxxxxx et al., 2008), building datasets for question answering (Xxxxxxx & Xxxx, 2008), extracting key phrases from documents (Xxxx et al., 2009), evaluating machine translation output (Xxxxxxxx-Xxxxx, 2009) and gathering user preferences for evaluating search engine output (Xxxxxxxxx et al., 2010; Xxxx & Xxxxxxxxx, 2010).
Sample 1
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