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It is because the amount of probable phrase sequences will increase, plus the patterns that tell effects become weaker. By weighting words and phrases within a nonlinear, distributed way, this model can "master" to approximate terms and never be misled by any unidentified values. Its "understanding" of a given phrase is just not as tightly tethered