  Online content analysis employs algorithmic methods to identify entities in
unstructured text. Both machine learning and knowledge-base approaches lie at
the foundation of contemporary named entities extraction systems. However, the
progress in deploying these approaches on web-scale has been been hampered by
the computational cost of NLP over massive text corpora. We present SpeedRead
(SR), a named entity recognition pipeline that runs at least 10 times faster
than Stanford NLP pipeline. This pipeline consists of a high performance Penn
Treebank- compliant tokenizer, close to state-of-art part-of-speech (POS)
tagger and knowledge-based named entity recognizer.
