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.
@article{arxiv.1301.2857,
title = {SpeedRead: A Fast Named Entity Recognition Pipeline},
author = {Rami Al-Rfou' and Steven Skiena},
journal= {arXiv preprint arXiv:1301.2857},
year = {2013}
}