Natural Language processing (NLP) systems are typically characterized by a pipeline architecture in which several independently developed NLP tools, connected as a chain of filters, apply successive transformations to the data that flows through the system. Hence when integrating such tools, one may face problems that lead to information losses, such as: (i) tools discard information from their input which will be required by other tools further along the pipeline; (ii) each tool has its own input/output format.
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