Many historical perspectives identify the genesis of Natural Language Processing with Alan Turing’s efforts during World War II cracking the Nazi’s Enigma code machine. By any measure, his Ultra intelligence and Turing machine saved millions of lives and shortened the war by multiple years.
After World War II, Turing’s direction involved establishing the foundation of NLP in his article, “Computing Machinery and Intelligence.” This ground-breaking article is viewed as the first treatise on artificial intelligence. He proposed the “Turing test,” (see below) for addressing the question,” can machines think?” He supported his position by rebutting nine arguments against intelligent machines. The nutshell result -thinking machines do not just isolate the words; they identify what the words mean in context.
“A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.” – Alan Turing
Building on this foundation, language translation, language theory, probabilistic and data driven models have yielded what we know of as NLP today. Using NLP, computers dissect, absorb and draw meaning from language in context by:
- Decomposing posts, text, paragraphs and sentences into meaningful words
- Applying a lexicon containing words, expressions and symbols
- Transforming words into a grammatical structure which shows how the words are related
- Defining word structures and their meaning from their context
- Abstracting language meaning for social situations by applying rules
It is surprising how many everyday situations benefit from the application of NLP. Some of them include:
- Web search – allowing algorithms to read text on a page and translate to another language
- Word processing – supporting grammar and spelling checks
- Translation – computer applications to translate speech or text into another natural language
- Speech recognition – decoding the human voice for mobile telephony, virtual assistance
- Summarization – condensing a text source into a shorter version
- Sentiment analysis – analyzing text before distribution, analyzing customer/product feedback
NLP and Sentiment Analysis
Natural Language Processing as part of the artificial intelligence discipline is the foundation for sentiment analysis. Both supervised and unsupervised learning techniques can be applied to complete sentiment analysis. The approach being used relies on the input word, sentiment values definition, and the level of control desired in understanding, modifying and acting on the results.
Natural Language Processing’s power is derived by its ability to understand and manipulate the human language. This ability delivers exact answers to questions without extraneous information. In addition, NLP can provide structure and sequence to ambiguous information. Yalo utilizes these functions as part of our new Sentiment Analysis services for expanded social media success for our clients.