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  • Networked Human Computation for Advanced Language Understanding
    Networked human computation (NHC) is a method of solving computational problems by breaking them down into smaller tasks that can be performed by humans over a network. This approach has been used successfully to solve a variety of problems, including image classification, natural language processing, and data entry.

    Computer language comprehension (CLC) is the ability of a computer to understand the meaning of human language. This is a challenging task, as human language is often ambiguous and complex. However, NHC has the potential to make CLC much more feasible.

    By breaking down the task of CLC into smaller tasks, such as identifying parts of speech, determining the meaning of words, and resolving anaphora, NHC can make it possible for computers to understand human language in a more robust and accurate way.

    While NHC has the potential to solve CLC, there are also a number of challenges that need to be overcome. One challenge is the need for a large number of human workers to perform the tasks required for NHC. Another challenge is the need to ensure that the work performed by human workers is accurate and reliable.

    However, despite these challenges, NHC has the potential to revolutionize the way that computers interact with humans. By making it possible for computers to understand human language, NHC could open up new possibilities for human-computer interaction, making it easier for people to communicate with computers and access information.

    Here are some specific examples of how NHC could be used to solve CLC:

    * Identifying parts of speech: Humans could be asked to identify the parts of speech of words in a sentence. This information could then be used to parse the sentence and determine its structure.

    * Determining the meaning of words: Humans could be asked to provide definitions for words. This information could then be used to build a dictionary that the computer could use to understand the meaning of words.

    * Resolving anaphora: Humans could be asked to identify the antecedents of pronouns and other anaphoric expressions. This information could then be used to make sure that the computer understands the correct meaning of these expressions.

    By combining these and other techniques, NHC could help computers to understand human language in a more robust and accurate way. This could open up new possibilities for human-computer interaction, making it easier for people to communicate with computers and access information.

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