document: High-precision phrase-based document classification on a modern scale We present a document classification system that employs lazy learning from labeled phrases , and argue that the system can be highly effective whenever the following property holds : most of information on document labels is captured in phrases . We call this property near sufficiency . Our research contribution is twofold : ( a ) we quantify the near sufficiency property using the Information Bottleneck principle and show that it is easy to check on a given dataset ; ( b ) we reveal that in all practical cases -- from small-scale to very large-scale -- manual labeling of phrases is feasible : the natural language constrains the number of common phrases composed of a vocabulary to grow linearly with the size of the vocabulary . Both these contributions provide firm foundation to applicability of the phrase-based classification ( PBC ) framework to a variety of large-scale tasks . We deployed the PBC system on the task of job title classification , as a part of LinkedIn 's data standardization effort . The system significantly outperforms its predecessor both in terms of precision and coverage . It is currently being used in LinkedIn 's ad targeting product , and more applications are being developed . We argue that PBC excels in high explainability of the classification results , as well as in low development and low maintenance costs . We benchmark PBC against existing high-precision document classification algorithms and conclude that it is most useful in multilabel classification .
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