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We designed and developed a multilingual algorithm which detects potential export projects before the are getting on tender platforms.
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With the sheer amount of news articles published online every day, it's impossible for humans to sift through them all to identify what's worth reading. That's where machine learning comes in. Machine learning algorithms can quickly scan through thousands of articles, identify the most important ones, and even summarize them.
Machine learning algorithms can quickly scan through thousands of articles, identify the most important ones, and even summarize them.
With the use of embeddings and active learning, machine learning models our model is more accurate and better equipped to handle new and unfamiliar types of news articles.
How it works?
One way that machine learning can detect news is through the use of embeddings. An embedding is a numerical representation of a word that captures its semantic meaning.
By using embeddings, machine learning models can understand the context of a word in a sentence or article and identify patterns that indicate whether the article is news or not. For example, the word "breaking" might be a strong indicator that an article is news.
The model is trained to recognize patterns that indicate whether an article is news or not. Once the model is trained, it can be used to scan through new articles and identify which ones are likely to be news.
The model can also be used to summarize articles, making it easier for humans to quickly understand the key points of a news story.
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Active learning is a process where a machine learning model is trained to ask questions to humans to learn more about the data it's analyzing. For example, a machine learning model might ask a human whether an article is news or not, and then use that feedback to improve its accuracy. Active learning can help improve the accuracy of a machine learning model over time, especially when the model encounters new and unfamiliar types of news articles.
Detecting news in practice
Detecting news with machine learning involves several steps.
The machine learning model is trained on a dataset of a few labeled news articles. From this labeled articles we generated more synthetical data by using the newest text generation methods to enrich the dataset for an ensemble classifier.
The model is trained to recognize patterns that indicate whether an article is news or not. Once the model is trained, it can be used to scan through new articles and identify which ones are likely to be news.
The model can also be used to summarize articles, making it easier for humans to quickly understand the key points of a news story.
Data fetching and deduplication with LSH algorithm can be used to identify and remove duplicate data points efficiently in a wide range of applications beyond news detection.
Outlier detection using machine learning algorithms can help identify anomalies in large datasets, which is useful for fraud detection and cybersecurity applications.
Spam detection can be implemented using machine learning models to filter out unsolicited or unwanted messages in email or social media platforms.
Synthetic text generation with generative methods can be used to create labeled data for training machine learning models in a wide range of text analysis tasks, including sentiment analysis, topic modeling, and language translation.
Multistage classification with ensemble classifiers for each user and active learning pipeline to improve classifiers over time based on users' interests can be used in personalized recommendation systems for online shopping or entertainment platforms.
Overall, detecting news in traditional media or on Twitter with machine learning is an exciting and rapidly evolving field.
As the amount of news published online continues to grow, the role of machine learning in detecting and summarizing news will only become more important.
Whether you're dealing with Excel sheets, SQL databases, or other data structures, our cuttingedge AI technology can make your workflow more efficient, saving costs and boosting productivity across your entire organization. Say goodbye to tedious data searches and hello to seamless communication with your data.
