Other Services

When we search for a particular topic on the internet, the search result contains both relevant and irrelevant news articles. In this experiment, we explored developing a NLU-based algorithm to identify the relevance score of the resulting news articles for the searched topic. The relevance level helps us filter the results and reduces the efforts in manual filtering.

Supervised machine learning algorithms require large amounts of labeled data before they start giving useful results. But when labeled data is limited, these algorithms don’t generalise well to data they haven’t seen before. They also need a lot of domain expertise. In the current experiment, we have built a model to find the relevance of a news article for a specific topic with minimal labeled data. 

Another challenge is that since the topics are not fixed, we can’t use the traditional ML approach to solve this problem, as the number of classes are unknown at the time of training. We have used Natural Language Understanding(NLU) to tackle these issues. NLU is a subset of natural language processing that uses the semantic analysis of text to understand the meaning of sentences.

• Avoid scams by acting locally or paying with PayPal
• Never pay with Western Union, Moneygram or other anonymous payment services
• Don't buy or sell outside of your country. Don't accept cashier cheques from outside your country
• This site is never involved in any transaction, and does not handle payments, shipping, guarantee transactions, provide escrow services, or offer "buyer protection" or "seller certification"