Natural Language Processing (NLP) is one of the most vital fields of Artificial Intelligence. NLP is already commercially relevant. The NLP software market is set to reach the 26.4 billion mark by 2024 with a CAG rate (Compound Annual Growth Rate) of over 20%. Why is NLP so important for businesses? It is because NLP software will give your machines the ability to interact with human languages, detecting value and emotions.
Some remarkable use cases of NLP software include -
· Health Industry – NLP programs help medicine experts extract and categorize disease conditions from patient notes. These programs process patient notes to predict medications, determine treatment outcomes, provide on-the-spot clinical test reports and carry out other mundane tasks such as daily maintenance of electronic health records.
· Customer Service - NLP programs use sentiment analysis to determine customer emotions towards brands, products, service; etc. Sentiment Analysis tools identify communication patterns by extracting data from feedback, social media comments, etc. Businesses use these assessments as valuable decision drivers for their customer interactions.
· Office Administration – NLP software can be used by companies as a personalized cognitive assistant. This AI and NLP-powered assistant will adapt to the existing organizational structure and constantly improve. The software catalogues internal organizational data acting as a personalized search engine for members of the workforce. Result? Increased output over shorter periods of time.
· Communication Management – Google uses NLP filters to classify emails as ‘Spam’, or ‘Important’, or as other prototypes even before they enter your inbox.
· Finance - Financial experts use NLP to track data (news, prices, etc.). NLP software is used as trading algorithms to improve and automate their investments.
· HR – HR companies use software that combines AI and NLP to better identify the skills of potential employees. HR recruiters can spot and scout talents with increased efficiency.
What features to look for in NLP Software
The growing demand for intelligent devices and predictive analytics drives the NLP market. Businesses can use man power for their product-centric approaches while creating a completely sustainable customer-centric approach with the help of NLP. Based on your business needs, here are the key departments you’ll encounter in the Natural Language Processing industry-
· Sentiment Analysis
· Text Classification
· Answering Customer Queries
· Summarization of massive amounts of Data
· Machine Translation
Applying all of these NLP tools is what businesses aiming to achieve optimal growth should look for. If you’re looking to team up with a provider of NLP, make sure that they provide -
Sentiment analysis is the automated process in which the NLP software interprets and assigns emotions to text data. Sentiment Analysis combines NLP and Machine Learning techniques.
Some of the most commonly used sentiment analysis models include –
· Fine-grained SA to assess millions of news articles, social media data, etc.
· Emotion detection to help assess customer feedback
· Aspect-based SA to focus on specific data issues
· Multilingual SA to analyze and process unstructured data in multiple languages.
Data comes in two forms – structured and unstructured. On an average, 10% of business data is structured as in they are recorded using pre-defined data models. Extracting information from structured data is easy. AI-powered NLP models are now able to process unstructured data that doesn’t come in the form of pre-set data models. With NLP software, you can use 90% of your unstructured organizational data. NLP software picks apart unstructured data, segmenting it into text, dates, and other categories. There are various types of NLP-powered data extraction models. Logical Extraction, Physical Extraction, and Automated Extraction are some of the most common ways of extracting structured data. Your NLP software creator must install faultless data extraction models.
The Boolean queries model is the oldest and the most used information extraction model. These models follow the set theory and are designed using principles of Boolean algebra.Text documents (customer queries, opinions, etc.) are treated as Boolean expressions.
Language detection is one of the most important use cases of NLP. It combines machine learning techniques for text classification and translation. Language detection models instantly recognize languages. Google Translate for instance is based on NLP technology. NLP software helps businesses rapidly categorize and organize information. On top of this processing, additional workflow layers that are language specific are applied. Language detection and translation models are nearing perfection.
Language detection is driving product innovation in the NLP software market. NLP software and the multilingual text analytics markets combine to provide vendors with the perfect chance to expand their customer bases. For example, the leading NLP firm Rosoka Software recently launched a Text Analytics tool, which can be used to examine unstructured documents in more than two hundred languages. Thanks to companies like Rosoka, it is easier for smaller businesses to expand their audience reach globally.
Rosoka: Industry-leading NLP Software
Be it their mastery of language detection/translation or their NLP-powered Geospatial Analysis, when it comes to NLP, Rosoka’s models have been pioneering the NLP software industry. The company offers instant solutions for businesses. Their software apps are often modified for government use as well. The highly-rated Rosoka Series 6 is transforming the process of sentiment analysis vastly improving time and cost-efficiency.
The NLP discipline is to focus on the relationship between human languages and data. NLP practitioners are able to achieve significant results in critical industries like finance and healthcare. Top NLP companies like Rosoka use their access to overwhelming amounts of data coupled with AI-powered computational skills to create perfect systems for businesses.