What Is Natural Language Processing?
Whether it’s Alexa, Siri, Google Assistant, Bixby, or Cortana, everyone with a smartphone or smart speaker has a voice-activated assistant nowadays. Every year, these voice assistants seem to get better at recognizing and executing the things we tell them to do. But have you ever wondered how these assistants process the things we’re saying? They manage to do this thanks to Natural Language Processing, or NLP.
Historically, most software has only been able to respond to a fixed set of specific commands. A file will open because you clicked Open, or a spreadsheet will compute a formula based on certain symbols and formula names. A program communicates using the programming language that it was coded in, and will thus produce an output when it is given input that it recognizes. In this context, words are like a set of different mechanical levers that always provide the desired output.
This is in contrast to human languages, which are complex, unstructured, and have a multitude of meanings based on sentence structure, tone, accent, timing, punctuation, and context. Natural Language Processing is a branch of artificial intelligence that attempts to bridge that gap between what a machine recognizes as input and the human language. This is so that when we speak or type naturally, the machine produces an output in line with what we said.
This is done by taking vast amounts of data points to derive meaning from the various elements of the human language, on top of the meanings of the actual words. This process is closely tied with the concept known as machine learning, which enables computers to learn more as they obtain more points of data. That is the reason why most of the natural language processing machines we interact with frequently seem to get better over time.
To illuminate the concept better, let’s have a look at two of the most top-level techniques used in NLP to process language and information.
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Tokenization
Tokenization means splitting up speech into words or sentences. Each piece of text is a token, and these tokens are what show up when your speech is processed. It sounds simple, but in practice, it’s a tricky process.
Let’s say that you are using text-to-speech software, such as the Google Keyboard, to send a message to a friend. You want to message, “Meet me at the park.” When your phone takes that recording and processes it through Google’s text-to-speech algorithm, Google must then split what you just said into tokens. These tokens would be “meet,” “me,” “at,” “the,” and “park”.
People have different lengths of pauses between words, and other languages may not have very little in the way of an audible pause between words. The tokenization process varies drastically between languages and dialects.
Stemming and Lemmatization
Stemming and lemmatization both involve the process of removing additions or variations to a root word that the machine can recognize. This is done to make interpretation of speech consistent across different words that all mean essentially the same thing, which makes NLP processing faster.
Stemming is a crude fast process that involves removing affixes from a root word, which are additions to a word attached before or after the root. This turns the word into the simplest base form by simply removing letters. For example:
“Walking” turns into “walk” “Faster” turns into “fast” “Severity” turns into “sever”
As you can see, stemming may have the adverse effect of changing the meaning of a word entirely. “Severity” and “sever” do not mean the same thing, but the suffix “ity” was removed in the process of stemming.
On the other hand, lemmatization is a more sophisticated process that involves reducing a word to their base, known as the lemma. This takes into consideration the context of the word and how it’s used in a sentence. It also involves looking up a term in a database of words and their respective lemma. For example:
“Are” turns into “be” “Operation” turns into “operate” “Severity” turns into “severe”
In this example, lemmatization managed to turn the term “severity” into “severe,” which is its lemma form and root word.
NLP Use Cases and the Future
The previous examples only begin to scratch the surface of what Natural Language Processing is. It encompasses a wide range of practices and usage scenarios, many of which we use in our daily lives. These are a few examples of where NLP is currently in use:
Predictive Text: When you type a message on your smartphone, it automatically suggests you words that fit into the sentence or that you’ve used before. Machine Translation: Widely used consumer translating services, such as Google Translate, to incorporate a high-level form of NLP to process language and translate it. Chatbots: NLP is the foundation for intelligent chatbots, especially in customer service, where they can assist customers and process their requests before they face a real person.
There’s more to come. NLP uses are currently being developed and deployed in fields such as news media, medical technology, workplace management, and finance. There’s a chance we may be able to have a full-fledged sophisticated conversation with a robot in the future.
If you’re interested in learning more about NLP, there are a lot of fantastic resources on the Towards Data Science blog or the Standford National Langauge Processing Group that you can check out.
