Deep or Shallow, NLP is Breaking Out

One of the featured speakers at the inaugural Text By The Bay conference, held in San Francisco in April 2015, drew laughter when describing a neural network question-answering model that could beat human players in a trivia game. While such performance by computers is fairly well known to the general public, thanks to IBM's Watson cognitive computer, the speaker, natural language processing (NLP) researcher Richard Socher, said, the neural network model he described "was built by one grad student using deep learning" rather than by a large team with the resources of a global corporation behind them. Socher, now CEO of machine learning developer MetaMind, did not intend his remarks to be construed as a comparison of Watson to the academic model he and his colleagues built. As an illustration of the new technical and cultural landscape around NLP, however, the laughter Socher's comment drew was an acknowledgment that basic and applied research in language processing is no longer the exclusive province of those with either deep pockets or strictly academic intentions. Indeed, new tools and new techniques--particularly open source technologies such as Google's word2vec neural text processing tool--combined with steady increases in computing power, have broadened the potential for natural language processing far beyond the research lab or supercomputer.