pdf AD HOC DIFFICULTY ESTIMATION FOR CROWDSOURCING SEMANTIC SIMILARITY Популярные

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Усталов Дмитрий Алексеевич

Екатеринбург, Россия

The phenomenon of crowdsourcing is often called artificial artificial intelligencedue to the possibility of emulating the computation systems’ performance withhuman workers. Such a mechanistic approach is applied in Crowdforge that representshuman-computed MapReduce [1], by the CrowdDB extending the SQLsyntax with the keyword CROWD that submits the corresponding tasks to a crowdsourcingplatform [2], by the human-assisted data analysis system CDAS [3], etc.Nevertheless, online crowdsourcing is a quite complex system that still has manyunexploited opportunities [4].Since modern natural language processing methods require many statisticaldata to train a model, it is becoming much more difficult to process Russian,a resource-poor language [5]. Hence, it is reasonable to apply crowdsourcing forfacilitating the language resources.However, many researchers agree that one of the most challenging problemsin crowdsourcing is the cold start problem [6]. The cold start problem may beaddressed by estimating parameters using information from the task domain.The present study is dedicated to this problem and is focused on a quite simplehuman computation task—semantic similarity assessment.