Have you ever thought about how a machine can automatically translate between natural languages? When I first heard about the topic of natural language processing, I had no idea how the two concepts of computers and linguistics could be related. Natural language processing is related to quite a variety of fields, such as computational linguistics, computer programming, and artificial intelligence.
So what exactly is natural language processing? NLP is the ability of a computer program to understand and comprehend the human language. The main goal of natural language processing is removing specific computer programming languages, such as C, Java, or Ruby, and have a language that is considered “human”. Natural language processing deals with analyzing, understanding and creating the languages that humans use in order to interact with computers in both verbal and written situations. All while using natural human languages instead of computer programming languages.
One of the main challenges of natural language processing is training computers to understand the way humans learn and use language. When developers create a program, they must make sure that the computer’s programming language is accurate, unambiguous, and organized. But when it comes to the human language, it may not always be so precise. If you take a look at the English language, you will notice that it can be very ambiguous and the structure of it depends on things like regional dialects and social contexts.
According to searchcontentmanagement.techtarget.com, common NLP tasks in software programs consist of:
- Sentence segmentation, part-of-speech tagging and parsing (“The process of marking up a word in a text as corresponding to a particular part of speech, based on both its definition, as well as its context”).
- Deep analytics (“A process that analyzes, extracts and organizes large amounts of data in a form that is acceptable, useful and beneficial”).
- Named entity extraction (“A sub-task of information extraction that seeks to locate and organizes elements in text into categories that are already pre-defined”).
- Co-reference resolution (“The task of finding all expressions that refer to the same entity in a text”).
Modern approaches to natural language processing are based on machine learning. Machine learning is a form of artificial intelligence that provides computer programs the ability to learn, grow and change when exposed to new data.
Application areas within natural language processing include machine translation between languages, dialogue systems (which allows for a person to interact with a machine using natural languages), and information extraction (where the goal is to transform unstructured text into structured representations, like a database, that can be searched and browsed in flexible ways), as stated by coursera.org.
Natural Language Processing technologies are having an enormous impact on the way humans interact and communicate with computers. NLP technologies are also impacting how people interact with each other through the use of language. Apple’s Siri is a prime example of where natural language processing is taking us, but it is only a small snippet of what researchers, involved in natural language processing, hope to achieve in the near future.