What Ethersource has Learned About Al-Qaeda in the Past Few Days
- This post gives examples of Ethersource’s learning capabilities.
- It gives examples of automatically learned topics and senses of the use of the term Al-Qaeda in English social media.
Ethersource is continuously exposed to massive text streams. On a given day, it sees millions of blog posts, tweets, and forum posts. And it learns. It gobbles up information much the same way a human picks up new ways of using new language constructs. Ethersource learns how the terms it reads are related to each other. It learns about topicality, and it learns about the different senses of the terms.
As an example, let’s have a look at what Ethersource has learned regarding Al-Qaeda the past few days. Topicality-wise, the texts concerning Al-Qaeda are described by Ethersource using the following terms:
To us humans, possessing the background knowledge imposed on us in media over the past decade, these terms come as no surprise. They all make sense as describing Al-Qaeda. Ethersource, however, has learned these topics from scratch, without access to any prior knowledge.
Furthermore, Ethersource has discovered two distinct senses, or meanings, of the term Al-Qaeda, as it has been used in social media during the past couple of days.
- The first sense of Al-Qaeda was automatically labelled PKK. In this sense, Al-Qaeda is related to Turkish, terrorists, militants, and fighters.
- The second sense of Al-Qaeda was automatically labelled Syria. In this sense, Al-Qaeda is related to Iran, Libya, Turkey, Tunisia, and fighting.
Unsupervised topic detection and sense discovery are both inherent properties of the semantic representation at the core of Ethersource. This makes for a powerful tool for an analyst when forming an understanding of the use of target concepts, be it in brand management, Open Source Intelligence, or sudden swings in World Markets.
We conclude this post with the observation that Ethersource has recently learned a new synonym of “Obama”: Obameat.