As part of our ongoing effort to provide the best solutions to our customers, we collaborate with research centers in the following areas:
Extracting structured information from an unstructured or semi-structured machine-readable document is referred to as Information extraction (IE), which is mostly done on natural language texts. Examples of Information Extraction from social media can be discovering the general public opinion about a movie, or a president speech through comments and posts on social networks and weblogs. The emergence of the user written web content created a greater need for the development of IE systems to assist people in dealing with the large amount of online data. These systems should be scalable, flexible, and efficiently maintained.
Ensuring privacy of users of social networks is probably an unsolvable conundrum. It seems, however, that informed use of the existing privacy options by the social network participants may alleviate - or even prevent - some of the more drastic privacy-averse incidents. Unfortunately, recent surveys show that an average user is either not aware of these options or does not use them, probably due to their perceived complexity. Furthermore, in social networks, users routinely share personal information. In such sharing, they might inadvertently reveal some personal health information, an essential part of their private information.
In the following three areas we are actively enhancing our customers experience:
We welcome talented individuals to join our team. If you are interested, please send us your resume at firstname.lastname@example.org
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