Natural Language Processing (almost) from Scratch
Table 16: Runtime speed and memory consumption comparison between state-of-the-art systems and our approach (SENNA). We give the runtime in seconds for running both the POS and SRL taggers on their respective testing sets. Memory usage is reported in megabytes.
['[BOLD] POS System', '[BOLD] RAM (MB)', '[BOLD] Time (s)']
All programs were run on a single 3GHz Intel core. The POS taggers were run with Sun Java 1.6 with a large enough memory allocation to reach their top tagging speed. The beam size of the Shen tagger was set to 3 as recommended in the paper. Regardless of implementation differences, it is clear that our neural networks run considerably faster. They also require much less memory. Our POS and SRL tagger runs in 32MB and 120MB of RAM respectively. The Shen and Toutanova taggers slow down significantly when the Java machine is given less than 2.2GB and 800MB of RAM respectively, while the Koomen tagger requires at least 3GB of RAM.