A Brief Introduction to Natural Language Processing for Software

The nature of the human language is ambiguous and nuanced. That’s precisely why computers have such a tough time processing it. They understand hard and fast data sets and rules that either don’t change or only change under given, pre-mapped sets of circumstances. Artificial Intelligence software developers needed to “bridge the gap” in order to move closer to a robot’s genuine understanding of human communication. Therefore, Natural Language Processing was conceived—borne out of the place where AI, computer science, and computational linguistics intersect. To put it simply, NLP is all about getting computers to understand and generate human language, and to understand the importance of its place in a world increasingly reliant on technology to drive innovation. 

The power of Natural Language Processing is its ability to parse out the hierarchical structure of language. Not all words within a sentence require the same amount of emphasis, for example, articles like "a" or "an" convey only specific, static meaning. As we all know, a given word doesn't even contain the same meaning each time it's used. Furthermore, words build upon themselves. They transform into a phrase, then a sentence, ultimately, culminating in an idea replete with sentiment and implication. NLP software uses two main techniques in order to derive meaning from language: 1) Syntactic Analysis and 2) Semantic Analysis. 

Syntactic Analysis Syntax is the grammatical arrangement of words in a sentence. This type of analysis uses computer algorithms to apply grammatical rules to phrases in order to extract meaning from them. Some of the most common syntactic analysis techniques are: 

Lemmatization— The identification of various inflicted forms of a word and their reduction into a single form for ease of analysis 

Parsing— Breaking a sentence down into its underlying grammatical parts, such as prepositional phrases, subjects, and objects 

Stemming—Cutting inflected words down to their root form and incorporating knowledge of their etymology into the larger syntactic analysis 

Semantic Analysis This portion of Natural Language Processing is the most problematic, and software developers are still working to fully master its application. For, if syntax is the “logos” of natural language processing, then semantics is the “pathos.” And, although artificial technology is developing rapidly, robots are still far better at thinking than they are feeling. The two most frequently used techniques for semantic analysis are: 

Named Entity Recognition—Determines the parts of a text that can be identified and categorized into groups that have been programmed to correlate with certain emotional responses, such as names of people and places 

Word Sense Disambiguation— Bestows meaning on a word based on its context 

Natural Language Processing will continue to expand its application across industries at a global level, and companies who invest in their own custom-tailored natural language processing model now will have a huge advantage in the new, tech-driven economy.