The Healthcare Analytics Summit is one of the best conferences held annually in the healthcare industry. It is a chance for medical professionals of all experience levels and no matter how many years they’ve been in the industry, to learn new things and stay up to date on new trends and innovations. Considering the rapidly growing technological climate, getting updated every year (through the healthcare analytics summit) is not nearly enough to keep track of the rapid growth of technology but it is at least a good place to start. One of the technologies that are being used at an increasing rate in the healthcare industry (and other industries as well) is Natural Language Processing.
If you are not up to date on the latest uses and types of natural language processing then chances are you are missing out on a lot of advantages. Based on artificial intelligence algorithms and driven by an increased need to manage unstructured enterprise information along with structured data, Natural Language Processing (NLP) is influencing a rapid acceptance of more intelligent solutions in various end‐use applications.
Whether it’s referred to as computational linguistics or text mining the goal for natural language processing is the same: to process everyday language and turn spoken words–text or speech–into structured data. To be able to analyze language for its meaning is a complex task. Technologies that treat language as anything but language—such as a sequence of symbols in pattern matching, or based on the distribution and frequency of keywords and co‐occurrences as with statistics methodologies, or even on language patterns as with shallow analysis (such as deep learning)—still seem to be a long way from achieving this goal. NLP algorithms that do not have an authentic comprehension of language will always be limited in their language understanding capabilities.
Uses of Natural Language Processing
Cognitive computing attempts to overcome these limits by applying semantic algorithms that mimic the human ability to read and understand.
Processing Enterprise Data
Natural Language Processing and its techniques are used a lot in the Enterprise Data World. It is largely applied in a variety of industry segments such as media & publishing, advertising, healthcare, banking & insurance and research to improve important enterprise activities, including:
Formulating Responses to Questions
Enterprise question answering tools leverage NLP algorithms to enhance customer experience and improve administrative activities by allowing users to ask questions in everyday language about products, services or applications and receive immediate and accurate answers. Virtual assistants (or virtual agents), for example, simulate a conversation with users to optimize customer support activities.
Social Media Monitoring
Social media monitoring represents a great opportunity for companies to know what their clients are talking about on social media platforms, blogs, etc. and to discover relevant information about their business. By interacting with clients, processing their conversations and essentially understanding customers in their own words, companies can better understand their customers’ needs and improve the relationships with them. It is also a tool used to monitor the social media activity of employees and potential employees, howbeit controversial.
Many organizations leverage natural language processing to approach text problems and improve activities such as knowledge management and big data analytics. Morphological, grammatical, syntactic and semantic analyses of language enable identification and extraction of different types of key elements (topics, locations, people, companies, dates, etc.), and generate the metadata that can be used to tag and categorize content in the most precise way.
Types of Uses of Natural Language Processing
Recurrent neural network (RNN)
A recurrent neural network (RNN), unlike a feedforward neural network, is a variant of a recursive artificial neural network in which connections between neurons make a directed cycle. It means that output depends not only on the present inputs but also on the previous step’s neuron state. This memory lets users solve NLP problems like connected handwriting recognition or speech recognition.
Shallow neural networks
Besides deep neural networks, shallow models are also popular and useful tools. For example, word2vec is a group of shallow two-layer models that are used for producing word embeddings. Presented in Efficient Estimation of Word Representations in Vector Space, word2vec takes a large corpus of text as its input and produces a vector space Every word in the corpus obtains the corresponding vector in this space. The distinctive feature is that words from common contexts in the corpus are located close to one another in the vector space.