High-precision phrase-based document classification on a modern scale

Ron Bekkerman, Matan Gavish

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Pages231-239
Number of pages9
DOIs
StatePublished - 2011
Externally publishedYes
Event17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11 - San Diego, CA, United States
Duration: 21 Aug 201124 Aug 2011

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Country/TerritoryUnited States
CitySan Diego, CA
Period21/08/1124/08/11

Keywords

  • High-precision classification
  • Large-scale classification
  • Multilabel text classification

ASJC Scopus subject areas

  • Software
  • Information Systems

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