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Machine learning methods are widely employed in natural language processing systems, to teach machines to understand human language in its context. Applying statistical techniques and predictive modelling, we can build trained systems for specific uses.
Content classification systems use statistical models to identify the category and classify content. Each classification category will have a set of words to which new data is compared and matched.
An ID3 based system to detect spammers. Identifying the optimal feature set is core to any spam detection problem. Here we use a neutral network to arrive at the feature set.
Plug and play search solution that uses advanced NLP techniques to process job queries and locate maximum number of matches.
NLP-based people search employing generic relation extraction and named entity recognition to identify relevant information from huge amounts of unstructured data.
Visualization of Wikipedia using Neo4J and D3. The interactive application uses machine learning techniques to classify, tag, and relate data.
Our personalization platform uses behavior profiling and collaborative filtering to provide contextual recommendations. User activities on a website are recorded and classified systematically to identify areas of interests. These are then matched with available content and suggested to the user.