SpeedRead: A Fast Named Entity Recognition Pipeline
Table 12: Speed of different NER taggers. SpeedRead is faster by 13.9 times using half the memory consumed by Stanford.
['[BOLD] NER Tagger', '[BOLD] Token/Sec', '[BOLD] Relative', '[BOLD] Memory']
SENNA achieves close accuracy with twice the speed and less memory usage. SpeedRead takes another approach by focusing on speed. We are able to speed up the pipeline to the factor of 13. SpeedRead’s memory footprint is half the memory consumed by the Stanford pipeline. Even though SpeedRead’s accuracy is not close to the state-of-art, it still achieves 18% increase over the CONLL 2003 baseline. Moreover, adapting the pipeline to new domains could be easily done by integrating other knowledge base sources as freebase or Wikipedia. SENNA and SpeedRead are able to calculate POS tags at the end of the NER phase without extra computation while that is not true of Stanford pipeline standalone NER application. Using Stanford corenlp pipeline does not guarantee better execution time.