What can NLP do for you?
How natural language processing can bring you success in your professional path
The basics of NLP and AI
Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables machines to understand the human language. It involves a lot of unstructured data and focuses on helping computers to understand the way humans write and speak. NLP also refers to the ability of a computer program to understand natural language as it is spoken and written.
How NLP helps LinkedIn members
NLP helps LinkedIn members by predicting the best responses to their queries, enabling AI applications to convert human-understood data into computer-understood data, and using deep learning-powered NLP to search user profiles and documents.
Examples of NLP applications
NLP has many applications, such as language translation, search engine results, smart assistants, customer service automation, email filters, survey analytics, form spell check, search autocomplete, search autocorrect, smart search, and grammar checkers.
Benefits of NLP for business
NLP can provide businesses with a variety of benefits, such as improved user-generated material tracking, the ability to detect and process large volumes of text data, email filters, voice recognition, and information extraction.
NLP applications for businesses
Common business applications of NLP include sentiment analysis, text classification, chatbots and virtual assistants, text extraction, speech pattern recognition, weather forecasting, healthcare applications, classifying handwritten documents, text summarization, and machine translation.
What hiring teams need to know about NLP
Recruiters should know that NLP is a field, not a skill, and that candidates should ideally have a Master’s or Ph.D. in the field to be proficient. However it can be argued that experience and the ability to learn new tools and technologies quickly is the main asset when it comes to the finding the ideal candidate. NLP can be used to transform recruitment by streamlining processes, revealing valuable insights from data, and freeing up time for meaningful human-to-human interactions. For example, you don't want to have to sift through hundreds of resumes, when you could use an AI solution to automatically provide you with the most relevant 5%.
How does deep learning work for NLP
Deep learning in natural language processing (NLP) involves using machine learning algorithms and models such as convolutional neural networks (CNN) or recurrent neural networks (RNN) to learn the rules for language analysis, as opposed to being taught rules. Deep learning is well-suited for NLP because it can capture sequence information better than other methods and allows for end-to-end training and representation learning.
What CNNs can do for you
CNNs can be used for recognizing patterns in vectors that are translated into numbers. CNNs consist of several layers of convolutions with nonlinear activation functions applied to the results. This allows them to learn hierarchical characteristics of the data and can be used for various classification tasks in NLP. For example, CNNs are widely used deep learning architectures in NLP for tasks such as sentence classification.
What RNNs can do for you
RNNs are a type of artificial neural network which uses sequential data. They are ideal for NLP tasks because they can represent temporal sequences and linguistic data such as text, time series, and audio. RNNs work by saving the output of a particular layer and feeding it back to the input in order to predict the output of the next layer. This allows them to treat words in context and process one word at a time while maintaining an internal memory of what it has already seen.
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