Entity Extraction Tools for Unstructured Text
Internet – the connectivity king has a global penetration of more than 60%. It means 6 in 10 people are posting their thoughts and sharing their likes/dislikes over the internet. This unstructured data can benefit many companies if they can somehow analyze it. One such process is entity extraction, a subset of natural language processing; it can extract useful information from a sizeable unstructured dataset.
Text entity extraction tools aim to annotate a text into categories or classes based on specific criteria. For example, traditional entities would constitute names, places, and things; however, modern systems have more complex entities, such as programming components. Entity recognition is done via different models, including natural language processing and AI-centric tools.
What is Entity Recognition?
An entity is the smallest measurable unit of a more extensive system. The system could comprise many entities, and the frequency of a single entity would vary. For example, a personal computer would include power cables, a mouse, a keyboard, etc. Here mouse, a cable, and a keyboard are entities.
Entity identification is a crucial element that aids us in understanding complex things. Human cognizance extracts the entities as valuable bits of information from a text to understand the context of anything. Entity recognition models and natural language processing tools also follow this scheme of thought.
Approaches to Entity Extraction:
Different approaches to entity extraction range from crude manual recognition to more complex deep learning models. Here we look at the different approaches:
- Machine Learning Models – extract entities by getting trained by experts to perform entity extraction. This training involves exposing the algorithm to various unstructured text sources. The training material can constitute hundred and thousands of unstructured texts. A statistical model is trained to extract entities by learning rules for entity identification. The process’s accuracy depends on the quality of training the algorithm provides.
- Matching Words – another entity extraction technique is matching words in the input query to those in the unstructured text. This technique, though, lacks the ability of nuance as it cannot differentiate between the fruit ‘orange’ and the color ‘orange.’
- Deep Learning Models – These models offer cutting-edge technological intervention to the entity extraction problem. Deep learning models can sift through unstructured texts without needing any training material. Entity detection is done via algorithms that learn as they go through the text and apply the same learned rules for entity recognition.
Named Entity Recognition and its Uses:
Named entity recognition is the process of identifying entities in unstructured text. Unstructured data exists all around us, and analyzing it is critical for any company’s success. Here are some possible domains in which entity recognition plays a huge part.
Improved Customer Services
Named entity recognition finds its uses in customer services and customer support. Responsive customer services are a godsend in this age, with the onus on productivity and selling. Customer support is often neglected instead of improving other processes, but customer services have been known to play a big part in a company’s success.
Entity recognition helps customer services by classifying customer tickets and complaints. The company can address the complaints by sending them to the relevant departments. It saves the cost of tagging the complaint manually and then trying to find the requisite department.
Customer feedback forms a vital loop that ensures quality assurance in a company’s products or services. Continuous improvement occurs when companies act on the customer’s feedback by improving processes and personnel based on such recommendations.
Entity recognition performs another critical function by tagging the location entities of different text. Companies can ascertain this way the location of negative feedback. Also, companies can classify users’ preferences according to location, and thus content recommendation becomes easier.
Finding the right candidate for a job can be a daunting task. Professionals with multiple degrees decide the fate of applicants but can still need to suggest the right candidate for the job. Named entity recognition can simplify this process at the resume stage by identifying certain key entities in the resumes.
Entity extractors find the most helpful information about a candidate as well as finding key custom entities that a recruiter may need. For example, an entry-level position will not need managerial skills, and recruiters may see such an entity as a red flag. Similarly, the required skills also form an entity that recruiters can search across resumes for the perfect candidate.
Entity Detection APIs and Tools:
Now that we have looked at entity detection and how entity extraction is helpful in various companies and businesses, let’s find out about the tools that perform entity detection.
Open-Source Named Entity Recognition APIs
The open-source application programming interface (API) is the first type of entity detection tool. Developers and coders typically use these APIs as they’re more flexible and free to use. The most used open-source NER APIs are:
- SNER (Stanford Named Entity Recognition) is a Java tool used ubiquitously for entity extraction. It uses Conditional Random Fields, thereby providing pre-trained models for key entities.
- SpaCy is a convenient model for users who want convenience with speed. It is a python-based framework based on statistical models to build custom entity recognition programs.
- NLTK (Natural Language Toolkit) is an excellent combination of libraries used in Python programs, mainly for natural language processing tasks. The program has its classification program but is adaptable to other APIs.
SaaS-Based Entity Recognition
Some of the best text entity extraction tools are SaaS-based because of their low coding requirements. It means that better-versed could use it in programming languages. Other benefits include cost-effectiveness and adaptivity with other platforms.
- MonkeyLearn is well-known for its custom training models and entity extraction solutions. It used named entity recognition models, amongst others, to analyze given text inputs.
- BytesView is another SaaS-based solution that can handle large volumes of unstructured texts. It offers ready-to-use models which are convenient for small businesses.
- Lexalytics also offers text analysis solutions for entity recognition.
VizRefra offers its users the best entity recognition models to analyze unstructured texts quickly. Entity recognition is an essential tool that can bolster a company’s profits, and we are well-equipped to steer you in the right direction.