Semantic Extraction with a Neural Network Architecture
It is the nineties again. Ronan Collobert from NEC Labs just released a noncommercial version of his neural network system for semantic extraction. Given an input sentence in plain english, Senna outputs a host of Natural Language Processing (NLP) tags: part-of-speech (POS) tags, chunking (CHK), name entity recognition (NER), and semantic role labeling (SRL). Senna does this with state-of-the-art accuracies, roughly two hundred times faster than competing approaches.
The Senna source code represents about 2000 lines of C. This is probably one thousand times smaller than your usual natural language processing program. In fact all the Senna tagging tasks are performed using the same neural network simulation code.