Businesses can extract information from semi-structured and unstructured data to enhance the efficiency of their daily operations by using named entity recognition applications.
Information extraction is the process of extracting structured information from semi-structured or unstructured data automatically. In most cases, information extraction involves processing human language in unstructured data so that machines can easily interpret it. Unstructured text data is rich with information, but it is not always easy to find what part of the data is relevant for a business. For instance, an online article about an event contains various pieces of information given in hundreds of words. But an event planning organization might only want relevant information like the location, organizer, and people mentioned in the article.
There are many techniques like named entity recognition, topic modeling, and natural language processing available for extracting information from unstructured data. Among them, named entity recognition is a method of extracting information from unstructured text data. It is a process of classifying unstructured text data into entities and categorizing it into different groups based on the information the entity is providing. Entities in context to named entity recognition are the nouns or a set of nouns extracted from a text. For example, in the text, “Rome was the center of the Roman Empire,” “Rome” and “Roman Empire” are entities. And this ability to segment unstructured text data based on entities has paved the way for several named entity recognition applications across industries, and they are helping business owners to enhance their daily operations.
Internet users upload, download, and share a tremendous amount of unstructured data daily. Hence, businesses are leveraging technologies like natural language processing and named entity recognition to extract information from such unstructured data that can help them enhance the efficiency of their operations.
Text classification has several applications like improving browsing on websites, automating CRM tasks, and developing an emergency response system. But, if algorithms extract each word of a document and classify them, then it will become a long process. Hence classifying each word will be time-consuming, and business administrators will have to allocate a lot of hardware resources for classification. It can also be costly as classifying all the text would require high storage, and business owners will have to invest more in storage devices. Further, productivity will also halt if hardware resources are put to classifying text and cannot perform any other tasks.
Named entity recognition systems can scan a document and classify only the major elements in it that are defined by and relevant to businesses. For instance, while scanning and monitoring online news websites, a named entity recognition system can classify the significant entities like people, organizations, events, and locations discussed in them. Classifying only the relevant text based on entities helps to enhance the task of text processing in natural language processing as it will have to focus primarily on the classified entities and not the entire text. And this ability to process text in real-time helps to speed up machine translation in virtual assistants so that they can provide quick responses to customer queries.
Chatbots have gained a lot of traction in recent years in the field of customer service. Named entity recognition, which is a part of natural language processing, is the backbone for creating chatbots that can provide accurate responses to customer queries. Although not a necessity, using natural language processing is important for creating chatbots. Chatbots need natural language processing and named entity recognition for natural conversation across languages, enhanced customer satisfaction, and improved sentiment analysis.
Named entity recognition also helps business administrators to provide improved response to customer complaints. For instance, let’s say a business has several branches across the globe, and a customer files an online complaint about one of its branches. It might be time-consuming and tedious for the administrators to transfer the complaint to the relevant branch. That’s where named entity recognition comes into the picture. Named entity recognition system can help classify the complaint based on the location, product, and person mentioned in it. And then, the complaint can be automatically transferred to the relevant branch and team without the need for any human interference.
In the digital era, companies are constantly gathering data about their customers to provide personalized recommendations. Customers themselves prefer getting personalized recommendations. According to a survey, 77% of consumers want personalized content. Named entity recognition helps power automated content recommendation systems as it can categorize products and contents with the help of text classification. For instance, it can categorize products based on their descriptions, and then, machine learning algorithms can further provide recommendations to customers based on the classification. Named entity recognition can also tailor the content according to customers’ preferences. Although consumers want recommended content, they don’t want to get overwhelmed by it. Sending the same email to the entire email list won’t do any good to businesses. Business owners should instead tailor the content of their emails to customers’ preferences, demographics, and locations before sending out the emails. And named entity recognition systems can segment customers based on classified entities.
Named entity recognition can also extract entities from the synopsis of a series and provide recommendations of other series with matching entities. For instance, Netflix is using a content recommendation system to recommend similar movies to viewers.
Semantic annotation is the process of attaching various pieces of information to concepts (for example, people, places, or things) of text. Unlike standard annotations, which are for readers’ reference, semantic annotations are for machines so that they can easily interpret human language. A typical process of semantic annotation includes text identification, text analysis, concept extraction, relationship extraction, and indexing and storing in the semantic graph database. Named entity recognition is used as a sub-process in the semantic annotation to analyze text. The next two processes of semantic annotation which are concept and relationship extraction are done based on entities that are classified with the help of named entity recognition.
With the help of semantic annotation, it becomes possible to auto-label data that is used by self-learning algorithms. For instance, tagging and annotation have been long used by journalists for categorizing their articles. Named entity recognition systems can tag articles with classified entities, and AI systems developed for auto-generating news articles can use them as a reference to learn how to tag their articles accurately. Accurate tagging of auto-generated articles will further help to generate more efficient news stories.
As the volume of digital data is growing every day, researchers and developers are finding it challenging to create named entity recognition applications that can accurately classify text data into different groups. Hence, they are trying to make named entity recognition systems that can classify text on a contextual basis. Creating such systems that are capable of classifying entities based on context will help to train AI models more accurately. Supervised and semi-supervised AI models require labeled data for training. Accurate named entity recognition systems can automate the process of labeling training data. Hence, it will reduce the labor hours required to tag and label training data and also minimize the risks of errors while labeling it.
Naveen is the Founder and CEO of Allerin, a software solutions provider that delivers innovative and agile solutions that enable to automate, inspire and impress. He is a seasoned professional with more than 20 years of experience, with extensive experience in customizing open source products for cost optimizations of large scale IT deployment. He is currently working on Internet of Things solutions with Big Data Analytics. Naveen completed his programming qualifications in various Indian institutes.
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