Crowdsourcing for linguistic data typically aims to replicate expert annotations using simplified tasks. But an alternative goal — one that is especially relevant for research in the domains of language meaning and use — is to tap into people's rich experience as everyday users of language. Research in these areas has the potential to tell us a great deal about how language works, but designing annotation frameworks for crowdsourcing of this kind poses special challenges. In this paper we define and exemplify two approaches to linguistic data collection corresponding to these differing goals (model-driven and user-driven) and discuss some hybrid cases in which they overlap. We also describe some design principles and resolution techniques helpful for eliciting linguistic wisdom from the crowd.
Spatial language understanding is important for practical applications and as a building block for better abstract language understanding. Much progress has been made through work on understanding spatial relations and values in images and texts as well as on giving and following navigation instructions in restricted domains. We argue that the next big advances in spatial language understanding can be best supported by creating largescale datasets that focus on points and paths based in the real world, and then extending these to create online, persistent playscapes that mix human and bot players, where the bot players must learn, evolve, and survive according to their depth of understanding of scenes, navigation, and interactions.
Edge defocus can be reduced by several techniques on a Nikon S204 scanner. By using main software MCSV version 3.44 or above, as well as disable range, and scan direction. Edge defocus can be reduced but is not eliminated at 3 o'clock and 4 o'clock. Wafer flatness is studied to investigate the cause of this defocus. The chuck shape is the key to avoid to defocusing at these two positions. Modification of chuck shape can reduce yield loss.
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