Natural Language Processing
What is Natural Language Processing?
Natural language processing (NLP) is a set of artificial intelligence techniques that enable computers to recognize and understand human language. It helps make computers more easily accessible for humans. Natural language processing uses computer science and computational linguistics to bridge the gap between human communication and computer comprehension. It does this by analyzing large amounts of textual data rapidly and understanding the meaning behind the command. Natural language processing enables computers to comprehend nuanced human concepts such as intent, sentiment, and emotion. It is similar to cognitive computing in that it aims to create more natural interactions between computers and humans.
Why is Natural Language Processing Important?
Humans comprehend words, phrases, and sentences largely through context and familiarity. For example, when we hear a certain phrase in one context, it may inspire a certain understanding. However, when we hear it around other words, it may take on a completely different meaning. This is how we can have sophisticated conversations with relative ease. This is the same concept behind natural language processing. Computers analyze text, words, or other data to parse the intent of the speech. The advantage is that computers can analyze much larger sets of data much faster.
Natural language processing applications include the following:
- Smart assistants like Alexa or Siri rely on natural language processing algorithms to function. These tools use voice recognition to understand what is said and match it to a useful response. Initially, this was used to identify someone alerting the assistant with “Hey Siri” or “Hey Alexa.” More recently, these tools have been able to understand context and create shortcuts or make other improvements. This new processing is also how these tools can identify jokes and respond with humorous answers.
- Transcription and translation on our phones are becoming increasingly common. A good example of this is when you get a call during a meeting. Your phone’s ability to transcribe a voicemail helps you identify important messages. Similarly, translation tools help us navigate new countries or new situations. Previously, people had to carry around translation dictionaries. Nowadays, we can simply speak into our phones and be understood in a matter of seconds.
- Spam and email filters rely heavily on natural language processing techniques to scan and classify emails. They analyze content for language common to spam or phishing emails. Examples of this include the use of financial terms, typographical errors, poor syntax or grammar, and threatening language. Another example is your inbox recognizing an email as primary, social, or promotional in nature. This filtering helps keep inboxes manageable and pushes more relevant emails to the forefront.
- Predictive text, along with autocorrect and auto-complete, helps us save time and increases accuracy in texts and emails. These tools use NLP to function and improve over time. For example, the more you use your phone, the better it will get at predicting the words you are starting to type. Predictive text can not only help semi-automate several repetitive steps, but can also ensure accuracy. Similarly, NLP can help improve search engine results and web page suggestions.
- Data and text analysis are crucial tools for business intelligence and other companies. Amassing data is meaningless unless it can be analyzed in actionable and meaningful ways. Natural language processing can help businesses enhance processes by using natural language to segment data. NLP can also be used to analyze social media comments and get more accurate understandings of customer interactions, for example.
These natural language processing examples demonstrate the value of this software discipline. This is due largely to the fact that natural language processing techniques, combined with machine learning, improve over time. In fact, deep learning in natural language processing can take these applications in bold new directions.
Natural Language Processing + DataRobot
DataRobot AI platform features a variety of NLP capabilities. If text features are detected in your dataset, DataRobot identifies the language and performs necessary preprocessing steps. For feature engineering with text data, DataRobot automatically finds, tunes, and interprets the best text mining algorithms for a dataset, saving both time and resources.
DataRobot’s capabilities include—but are not limited to—tokenization, data cleaning (stemming, stop word removal, etc.), and application of various vectorization methods. DataRobot AI platform supports n-gram matrix (bag-of-words, bag-of-characters) analytical approaches, as well as word embedding techniques, such as Word2Vec and fastText with both CBOW and Skip-Gram learning methods. Additionally, the platform can perform Naive Bayes SVM and cosine similarity analysis.
DataRobot is continuously expanding its NLP capabilities, including the latest language representation models like BERT (Google’s transformer-based de-facto standard for NLP transfer learning). Tiny BERT (or any distilled, smaller, version of BERT) is now available with certain blueprints in the DataRobot Repository. These blueprints provide pretrained feature extraction in the NLP field.
For visualization, there are word clouds for improved text analysis that allow users to see which words are impacting predictions made by a model (for binary classification, multiclass classification, and regression), view class-specific word clouds (for multiclass classification projects), filter out common stop words (for, was, or, etc.), and much more.