Natural Language Processing (NLP) for sustainable innovation
NLP on the rise
Natural Language Processing unlocks the ability of machines to read text, hear speech, and interpret words, and NLP technology has advanced greatly in the last five years. Many use cases have emerged for social and environmental innovation.
Chatbots & conversational AI
Tackling your NLP problems
Several organizations have solved their NLP problems with Omdena, ranging from policy analysis, social sentiment analysis to health and energy sector related use cases. Our diverse talent pool in combination with our collaborative platform has resulted in a track record of innovative and efficient solutions.
Example case studies
Understanding policy effects on vulnerable populations during pandemics
A team of 28 AI experts and data scientists collaborated to gauge the impact of pandemic policy implemented post-COVID-19 on vulnerable populations to find correlations and encourage data-driven policy making to lessen the adversity for the most vulnerable populations around the world.
A risk classifier for a chatbot that helps to assess Post-traumatic-stress-disorder (PTSD)
32 Omdena collaborators developed a machine learning driven chatbot for PTSD assessment in war and refugee zones. The unique aspect of the project was that only through the collaborative efforts of the project community, we could identify and annotate suitable patient data. The teams applied linear classifiers in NLP for risk assessment and transfer learning to augment the data.
Identifying land use conflicts events and matching mediating government policies
The team built a machine learning driven visualization app that matches land conflict events from news articles with mediating government policies. This enables policy makers to make data-driven decisions and resolve land conflicts faster, save resources, and facilitate environmental sustainability efforts.
The AI project was hosted by the World Resources Institute with a focus on India as a country struggling heavily with land disputes.
Analyzing the social sentiment of citizens toward the clean energy transition
The World Energy Council commissioned Omdena to explore the effectiveness of artificial intelligence to grasp the attitudes of the world’s populace on these topics.
For this eight-week machine learning project, the team built numerous AI models to perform natural language processing. The models were trained to gather and categorize public conversations about energy transition topics.
For example, one set of models gathered and analyzed tweets in more than 20 countries that were related to complaints about “renewable energy cost”.
Together we can solve the grandest problems
We’re committed to empowering organizations around the world to achieve their unique goals.
We’re really excited about the results of this project. My team currently uses the code and infrastructure on almost a weekly basis. This data helps supply chain professionals mitigate risk in regards to product-sourcing. Also, policymakers can check what works and where those policies work.