Will the language barrier ever be a thing of the past? With new advances in artificial intelligence like NLP, things are changing fast.
NLP—natural language processing—is an emerging AI field that trains computers to understand human languages. NLP uses machine learning algorithms to gain knowledge and get smarter every day. And it’s built into tools that many of us use daily, from spell checkers to smart speakers.
According to Forbes, we’re now in the “Golden Age of NLP.” There are even more advances on the horizon. Join us as we explore NLP and its impact on the translation industry.
Understanding NLP Technology
NLP takes raw, written text and interprets it into a form that a computer can understand and analyze. State-of-the-art NLP can quickly analyze large amounts of text to generate insights and complete different tasks. For example, instead of manually translating an entire website into a different language, you can use Google’s NLP-powered translation tool, Google Translate, to automatically draft a translation.
Uses of NLP
Natural language processing is a billion-dollar industry for a reason. Some researchers believe that NLP has the potential to revolutionize many industries, from healthcare to sales and marketing.
NLP is the backbone of many popular tools that a lot of us use every day. Here are just a few of them.
- Language Translation
AI language translation tools use NLP to translate written text or spoken words from one source language into a different target language.
- Virtual Assistants
Virtual assistants like Siri, Alexa, and Cortana use NLP to “speak” in full sentences and sound like humans. They also use NLP for speech recognition.
- Search Engines
Search engines like Google, Bing, Yahoo, Baidu, and Yandex use NLP to autofill search queries for you so you get the most relevant results.
- Writing Assistants
Tools like Grammarly and Microsoft Editor use NLP to check spelling, grammar, and syntax in text. They can also analyze the voice, tone, and formality of your writing.
- Autocomplete and Autocorrect
When you text a friend on your smartphone or send a message on LinkedIn, NLP provides predictive suggestions for you with autocomplete and autocorrect.
- Chatbots
Improvements in NLP have made it easier for companies to engage with customers and solve problems with AI-powered chatbots.
- Automatic Content Moderation
At times, social media can be notoriously toxic, but NLP is effective at performing sentiment analysis. This helps platforms automatically moderate their sites to identify and remove problematic content like bullying or slurs.
- Text-to-speech Tools
Text-to-speech tools use NLP to transcribe spoken content accurately and quickly.
How NLP Works
Natural language processing trains machines to learn and understand human language and speech. NLP and ML (machine learning) are part of AI (Artificial Intelligence). Both subfields share techniques, algorithms, and knowledge.
Ever wondered how it works? Here’s how NLP analyzes massive amounts of data to complete tasks for humans.
1: Preprocessing Data
To begin, NLP pre-processes texts to extract pertinent information. Here are just a few of the data science techniques that work behind the scenes of NLP preprocessing.
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- Tokenization
Tokenization analyzes sentences to break them down into smaller building blocks like words, numbers, or symbols. - Lemmatization and stemming
Lemmatization and stemming help AI understand the variants of a specific word. For example, if you search Amazon for shirts, Amazon should query its product database for both the plural and singular forms of the word “shirt.” - Part of speech (POS) tagging
POS tagging is a machine learning technique that helps AI understand parts of speech like nouns, pronouns, verbs, adverbs, etc. - Sentiment Analysis
This is used to scrutinize text to determine its emotional tone. For example, some companies use sentiment analysis to filter online reviews into positive or negative categories. - Named Entity Recognition (NER)
NER locates and classifies named entities in a body of text. NER can help identify and categorize names, organizations, locations, events, and dates. - Text Summarization
This NLP technique can concisely summarize a text. Summarizing a long text can be very time-consuming for a human, but NLP can do it in seconds.
- Tokenization
2: Analyzing and Classifying the Data with Algorithms
After the data is preprocessed, the NLP algorithm analyzes it. There are many different types of algorithms used in NLP, but the following types are the most common:
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- Rule-Based Systems
This type of system uses carefully crafted sets of linguistic rules and statistics to analyze data and complete tasks. - Machine Learning Systems
Machine learning algorithms perform tasks based on their training data. As they process more data, they adjust their methods. For example, neural technology employs artificial intelligence to learn and improve its knowledge constantly. In this way, it strives to mimic the neural networks in the human brain.
- Rule-Based Systems
NLP and Machine Translation
With thousands of different languages used all over the world, translation is one of the most exciting applications of natural language processing technology.
What is Machine Translation?
Machine translation (MT) is one of the oldest fields of AI research, and recent advancements in NLP have led to big improvements in translation quality. Machine translation is the process that a computer uses to translate text from one language to another, like English to Spanish, without human intervention.
The earliest versions of machine translation were plagued with inaccuracies and translation errors. In recent years, developments like neural machine translation (NMT) helped AI tools build their knowledge to craft more nuanced and accurate sentences. Google Translate, Microsoft Translate, DeepL, and IBM’s Watson use the latest NLP technology to power their machine translation systems.
Benefits of NLP Machine Translation
AI tools make the translation process easier than ever. A crucial benefit of machine translation is its lightning-fast processing power. Computers can now translate an entire book, website, or product database in mere seconds. Another primary benefit is cost. Many leading AI translation tools have low-cost enterprise versions for companies who wish to localize their websites.
Although NLP led to huge advancements in language translation, AI translations aren’t quite perfect yet. Machine translation can’t always understand cultural differences or translation context like human readers can. For this reason, human oversight is still needed for accurately translating content from one language to another.
In the translation industry, machine translation offers the most value as a tool to speed up the localization process. When used together with human post-editing, you get the best of both worlds.
Choosing the Right Translation Tools
As more companies go global, localization teams must work hard to meet the demand for localized content in international markets. NLP-powered machine translation technology reduces localization costs and helps translators produce content more quickly. However, until AI translations can completely imitate the human brain, post-editing from human translators will still be needed. It’s crucial to choose translation software that supports both NLP machine translation and human translators.
Why Use Localize?
Integrating the power of NLP into your localization strategy is a must. Localize is a translation management system (TMS) that is always keeping an eye on future technical developments that can improve the quality of translations.
Localize provides several cutting-edge machine translation options including Google Translate, Amazon Translate, and Microsoft Translate. Our tools also integrate with top LSPs and translation agencies so you can experience the benefits of both human and machine translation.
Contact the experts at Localize to explore how our solutions can help you.
