What is natural language processing (NLP)?

natural language processing

NLP is a principled approach to processing human language. Formally, it is a subfield of artificial intelligence (AI) that refers to computational approaches to process, understand, and generate human language. The reason it is part of AI is because language processing is considered a huge part of human intelligence. The use of language is arguably the most salient skill that separates humans from other animals.

What is NLP?

NLP includes a range of algorithms, tasks, and problems that take human-produced text as an input and produce some useful information, such as labels, semantic representations, and so on, as an output. Other tasks, such as translation, summarization, and text generation, directly produce text as output. In any case, the focus is on producing some output that is useful per se (e.g., a translation) or as input to other downstream tasks.

You might wonder why NLP explicitly has “natural” in its name. What does it mean for a language to be natural? Are there any unnatural languages? Is English natural? Which is more natural: Spanish or French?

The word “natural” here is used to contrast natural languages with formal languages. In this sense, all the languages humans speak are natural. Many experts believe that language emerged naturally tens of thousands of years ago and has evolved organically ever since.

Programming languages such as C and Python are good examples of formal languages. These languages are defined in such a strict way that it is always clear what is grammatical and ungrammatical. When you run a compiler or an interpreter on the code you write in those languages, you either get a syntax error or not. The compiler won’t say something like, “Hmm, this code is maybe 50% grammatical.” Also, the behavior of your program is always the same if it’s run on the same code, assuming external factors such as the random seed and the system states remain constant. Your interpreter won’t show one result 50% of the time and another the other 50% of the time. This is not the case for human languages. You can write a sentence that is maybe grammatical. For example, do you consider the phrase “The person I spoke to” ungrammatical? There are some grammar topics where even experts disagree with each other.

This is what makes human languages interesting but challenging, and why the entire field of NLP even exists. Human languages are ambiguous, meaning that their interpretation is often not unique. Both structures (how sentences are formed) and semantics (what sentences mean) can have ambiguities in human language. As an example, let’s take a close look at the next sentence:

He saw a girl with a telescope.

When you read this sentence, who do you think has a telescope? Is it the boy, who’s using a telescope to see a girl (from somewhere far), or the girl, who has a telescope and is seen by the boy? There seem to be at least two interpretations of this sentence as shown in figure.

The reason you are confused upon reading this sentence is because you don’t know what the phrase “with a telescope” is about. More technically, you don’t know what this prepositional phrase (PP) modifies. This is called a PP-attachment problem and is a classic example of syntactic ambiguity. A syntactically ambiguous sentence has more than one interpretation of how the sentence is structured. You can interpret the sentence in multiple ways, depending on which structure of the sentence you believe.

Why NLP?

If you are reading this, you have at least some interest in NLP. Why is NLP exciting? Why is it worth learning more about NLP and, specifically, real-world NLP?

The First reason is that NLP is booming. Even without the recent AI and ML boom, NLP is more important than ever. We are witnessing the advent of practical NLP applications in our daily lives, such as conversational agents (think Apple Siri, Amazon Alexa, and Google Assistant) and near human-level machine translation (think Google Translate). A number of NLP applications are already an integral part of our day-to-day activities, such as spam filtering, search engines, and spelling correction, as we’ll discuss later.

The Second reason is that NLP is an evolving field. The field of NLP itself has a long history. The first experiment to build a machine translation system, called The Georgetown–IBM Experiment, was attempted back in 1954. For more than 30 years since this experiment, most NLP systems relied on handwritten rules.

The Third and final reason is that NLP is challenging. Understanding and producing language is the central problem of artificial intelligence, as we saw in the previous section. The accuracy and performance in major NLP tasks such as speech recognition and machine translation got drastically better in the past decade or so. But human-level understanding of language is far from being solved.