Ontopo benefits include a comprehensive suite of advanced machine learning algorithms that can help organizations make sense of their data. These include entity recognition and disambiguation, sentiment analysis, topic modeling, and predictive analytics.
Ontopo also makes hiring workers easier by keeping all contracts in one place and allowing you to create new ones with just a few clicks. It also offers a convenient way to amend existing contracts.
Advanced Natural Language Processing
Natural language processing enables computers to understand and interpret real-world human input like text or speech. It’s also used to automate tasks that require human judgment, such as machine translation.
Search engines use NLP to recognize your query, and voice-activated software such as Siri or Alexa rely on NLP technology to respond to your questions. NLP can also help businesses retrieve pertinent information from unstructured data for enhanced data sets.
NLP techniques are used for a variety of purposes, including sentiment analysis, entity recognition and disambiguation, topic modeling and generating text. Some top advanced NLP techniques include: semantic analysis, co-reference resolution, text summarizations and machine translations. Learn more about the benefits of NLP and how to incorporate these tools into your business. NLP is essential for a successful AI project.
Entity Recognition and Disambiguation
Founded on the principles of knowledge representation and semantic technologies, ontologies provide a formal framework to model concepts and relationships within a domain. As a result, they empower AI algorithms to understand data at a more holistic level and enable more accurate analytics.
The goal of this task is to recognize and disambiguate named entities in short text based on their relationship with gold standard entity references stored in a knowledge base. This information can be extracted using a combination of local context and more global semantic features such as topic signatures, WordNet and gazetteer.
Traditionally, restaurants have struggled to manage customer cancellations and overbooking. Ontopo solved this problem by creating a web-based reservation system that allows customers to view the availability of tables and make reservations at their preferred restaurant – all without having to call or email the venue.
Sentiment analysis enables you to go beyond subjective opinions and insights in unstructured data (like survey responses or call center interactions) and turn them into actionable, objective data. It helps identify customer needs, find product improvement opportunities, and boost your bottom line.
It also lets you spot critical issues in real-time, such as a social media PR crisis or angry customers about to churn. This is possible thanks to the fact that sentiment models are able to process massive amounts of data in minutes, as opposed to hours or days for human analysts.
You can use text analytics tools like TextiQ to extract sentiment from the feedback you receive from your customers and analyze it. Then, you can compare the results with those of competitors and act accordingly.
In topic modeling, a set of documents (or corpus) is represented as a matrix where each value w ij represents the frequency of the word i in document d i. This matrix is used as the input to a model which generates topics (or latent Dirichlet allocation) in order to discover the semantically meaningful clusters in the data.
Some studies use a fixed number of topics while others rely on qualitative assessments to choose the best topic model. However, quantitative metrics can be misleading and researchers should carefully evaluate the quality of their model using a variety of criteria.
Identifying the key themes of your text data allows you to make data-driven decisions with confidence. Topic models are especially helpful when there are large amounts of data that would be impossible to read manually.
Predictive analytics uses techniques from statistics, data analytics, AI, and machine learning to forecast trends and problems. It helps businesses take proactive measures like predicting sales, customer behavior, and employee productivity to improve efficiency.
For example, Sephora and Harley-Davidson use predictive analytics models to identify high-value customers to prioritize targeted marketing campaigns. This reduces staffing costs and boosts revenue. Predictive analytics can also predict customer churn and help businesses develop strategies to retain customers.
Ontopo’s knowledge graphs provide a formal representation of domain-specific concepts and relationships, enabling machines to understand the meaning of information at scale. This, in turn, empowers intelligent and context-aware machine learning algorithms and enhances predictive capabilities. This way, ontopo supports smarter and more efficient data-driven applications across diverse business functions.