Everything you need to know
Computers don’t speak languages the way humans do. They communicate in machine code or machine language, while we speak English, Dutch, French or some other human language. Most of us don’t understand the millions of zeros and ones computers communicate in. And in turn, computers don’t understand human language unless they are programmed to do so. That’s where natural language processing (NLP) comes in.
What is natural language processing?
Natural language processing is a form of artificial intelligence (AI) that gives computers the ability to read, understand and interpret human language. It helps computers measure sentiment and determine which parts of human language are important. For computers, this is an extremely difficult thing to do because of the large amount of unstructured data, the lack of formal rules and the absence of real-world context or intent.
In recent years, AI has evolved rapidly, and with that, NLP got more sophisticated, too. Many of us already use NLP daily without realizing it. You’ve probably used at least one of the following tools:
- Spell checker.
- Spam filters.
- Voice text messaging.
Five basic NLP tasks
As we mentioned before, human language is extremely complex and diverse. That’s why natural language processing includes many techniques to interpret it, ranging from statistical and machine learning methods to rules-based and algorithmic approaches. There are five basic NLP tasks that you might recognize from school.
Part of speech tagging
One of the tasks of NLP is speech tagging. For every sentence, the part of speech for each word is determined. Part of speech is a category of words that have similar grammatical properties. For example, the word book is a noun in the sentence the book on the table, but it’s a verb in the sentence to book a flight. And a word like set can even be a noun, verb or an adjective.
There is a large number of words that can serve as multiple parts of speech, which makes it challenging for a machine to assign them the correct tags.
Lemmatization concerns removing inflectional endings only and reducing a word to its base form, which is also known as a “lemma”. Past tenses are changed into present and synonyms are unified. For example, the past tense ran is changed to run and the synonym best is unified into good.
Lemmatization uses a different approach than stemming to reach the root form of a word. For example, the lemma of caring is care, not car as it is with stemming.
The tokenization task cuts a text into smaller pieces called tokens. This process segments a chunk of continuous text into separate sentences and words, while at the same time removing certain characters, like punctuation. For example, this sentence split up into smaller tokens would look like this:
For example this sentence split up into smaller tokens would look like this
That pretty much looks the same, right? That’s because languages like English often separate words with a blank space, but not all languages do. In those languages, tokenization is a significant undertaking that requires deep knowledge of the vocabulary.
In English, too, blank spaces may break up words that actually should be considered one token. Think of city names like Los Angeles or San Francisco or the phrase “New York-based”.
Disambiguation is a task that has to do with the meaning of the words we use in human language. Some words have more than one meaning, and while reading, we select the meaning that makes the most sense in the given context. For example, the word bat can refer to the animal that flies around at night or the wooden or metal club that is used in baseball. And a bank can be a place where you go to open a current account or a piece of land alongside a body of water where you go fishing.
Humans communicate based on meaning and context. Semantics help computers identify the structure of sentences and the most relevant elements of a text in order to understand the topic that is being discussed. For example, if a text contains words like election, democrat and republican or budget, taxes and inflation, the computer understands that the topics discussed are American politics and economics.
Examples of natural language processing in practice
In recent years, because of the availability of big data, powerful computing and enhanced algorithms, natural language processing has been rapidly advancing and transforming businesses. It’s now widely used across an array of industries. We have listed some interesting examples below:
- NLP is widely used in the translation industry. Many localization companies use machine translation to help their translators work more efficiently. When the text is already largely translated by machine, it saves them valuable time and the number of words they can translate daily increases.
- Search engines use natural language processing to come up with relevant search results based on similar search behavior or user intent. By using NLP, the average person finds what they’re looking for.
- NLP is also used for email filters. The spam filter has been around for quite some time now, but Gmail’s email classification is one of the newer NLP applications. Based on the content of the emails that come in, Gmail now also recognizes to which of the three categories (primary, social or promotions) the emails belong. This helps users determine which emails are important and need a quick response, and which emails they probably want to delete.
- We also see the use of natural language processing in healthcare. It can be used for streamlining patient information or for apps that convert sign language into text. The latter enables deaf people to communicate with people who don’t know how to use sign language.
- NLP is even being used in the aircraft maintenance industry. It helps mechanics find useful information from aircraft manuals that have hundreds of pages, and it helps find meaning in the descriptions of problems reported by pilots or others working in the industry.
Ways we use NLP at Textmetrics
What the examples above show is that there are numerous ways that NLP can improve how your company operates. That’s because human interaction is the driving force of most businesses. When you’re not too familiar with AI and NLP, though, it can be quite challenging to do it right. And having employees manually analyze all of the content that your company produces is almost impossible.
At Textmetrics, we offer a number of tools that use natural language processing to help organizations analyze their content and provide suggestions for improvements.
- A spell checker enables everyone in your organization to create grammatically correct and error-free content.
- A tool to determine the language level of the content you’ve created. This is based on the European Language Framework.
- A tool to flag words that are gender-biased, providing suggestions and possible replacements based on the target audience you’re creating the content for.
- An algorithm-based program based on the needs of your organization to help you standardize your communication according to your corporate identity.
Are you curious to know more about these tools, or do you want to find out if they could be of use in your organization? Please let us know. Textmetrics is here to help!