Category Archives: Technologies

Technologies

MeaningCloud sponsors prize for Author Profiling Research

Author Profiling ResearchCLEF Initiative and Conference

MeaningCloud sponsors the prize to the best team at the 5th International Competition on Author Profiling Research, PAN@CLEF 2017. This competition is part of PAN (Plagiarism, Authorship and Social Software Misuse), a series of scientific events and shared tasks on digital text forensics. The 17th evaluation lab on digital text forensics will be held as part of the CLEF conference in Dublin, Ireland, on September 11-14, 2017.

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Recorded webinar: Why You Need Deep Semantic Analytics

Last July 13th we delivered our webinar “Why You Need Deep Semantic Analytics”, where we explained how to achieve a deep, automatic understanding of complex documents. Thank you all for your interest.

During the session we covered these items:

  • Automatic understanding of unstructured documents.
  • What is Deep Semantic Analytics? Comparison with conventional text analytics.
  • Where it can be applied.
  • Case study: due diligence process.
  • Ideal features of a Deep Semantic Analytics solution.
  • MeaningCloud Roadmap in Deep Semantic Analytics.

IMPORTANT: you can find a more literary explanation of some of the items we covered, including the due diligence practical case, in this article.

Interested? Here you have the presentation and the recording of the webinar.

(También presentamos este webinar en español. Tenéis la grabación aquí.)
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Deep Semantic Analytics: A Case Study

Scenarios that can benefit from unstructured content analysis are becoming more and more frequent: from industry or company news to processing contracts or medical records. However, as we know, this content does not lend itself to automatic analysis.

Text analytics has come to meet this need, providing powerful tools that allow us to discover topics, mentions, polarity, etc. in free-form text. This ability has made it possible to achieve an initial level of automatic understanding and analysis of unstructured documents, which has empowered a generation of context-sensitive semantic applications in areas such as Voice of the Customer analysis or knowledge management.

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Why you need Deep Semantic Analytics (webinar)

Achieve a deep, automated understanding of complex documents

Conventional Text Analytics enable a first level of automatic understanding of unstructured content, achieved through its ability to extract mentions of entities and concepts, assign general categories or identify the polarity of opinions and facts that appear in the text. However, these isolated information elements do not reflect the wealth of information provided by these documents and impose limitations when it comes to finding, relating or analyzing them automatically.

Deep Semantic Analytics represents a step beyond conventional text analytics by providing features such as snippet-level granular categorization, detection of complex patterns, and extraction of semantic relationships between information elements in the document.

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Is Cognitive Computing too Cool to Be True?

According to IBM, “Cognitive Computing systems learn and interact naturally with people to extend what either humans or machines could do on their own. They help human experts make better decisions by penetrating the complexity of Big Data.” Dharmendra Modha, Manager of Cognitive Computing at IBM Research, talks about cognitive computing as an algorithm being able to solve a vast array of problems.

With this definition in mind, it seems that this algorithm requires a way to interact with humans in order to learn and to think as they do. Nice, great words! Anyway, it is the same well-known goal of Artificial Intelligence (AI), a more common name that almost everybody has heard about. Why change it? Ok, when a company is investing at least $1 billion in something, it must be cool and fancy enough to draw people’s attention, and AI is quite old-fashioned. Nevertheless, machines still cannot think! And I believe it will take some time.

How does Cognitive Computing work? According to the given definition, to enable the human-machine interaction, some kind of voice and image processing solutions must be integrated. I am not an expert on image processing, but voice recognition systems, dialog management models and Natuking-640388_1280ral Language Processing techniques have been studied for a while. Even Question Answering methods (i.e. the ability of a software system to return the exact answer to a question instead of a set of documents as traditional search engines do) have been deeply studied. We ourselves have been doing (and still do) research on this topic since 2007, which resulted in the development of virtual assistants, a combination of dialogue management and question answering techniques. Do you remember Ikea’s example called Anna? In spite of the fame she gained at that time, she is not working anymore. Perhaps, for users, that kind of interaction through a website was not effective enough. On the other hand, virtual assistants like Siri, supported by an enormous company as Apple, are gaining attention. There are other virtual assistants for environments different from iOS but they are far less known, perhaps because the companies behind them are quite smaller than Apple.

Several aspects of the thinking capabilities required by the mentioned algorithm have to do with the concept of Machine Learning. There are a lot of well-known algorithms which are able to generate models from a set of examples or even from raw data (in the case of unsupervised processes). This enables a machine to learn how to classify things or to group items together, like a baby piling up those coloured geometric pieces. So, combining Machine Learning and NLP models it is possible for a machine to understand a text. This process is what we call Structuring Unstructured Data (much less fancy than Cognitive Computing). That is, making your information actionable. We have been working on this during several years, but now it is called cognitive computing.

So, as you might imagine, Cognitive Computing techniques are not different from the ones we have already developed; a lot of researchers and companies have been combining them. And, if you think about it, does it really matter if a machine thinks or not? The relevant added value of this technology is helping humans to do their job with all the relevant information at hand, at the right moment, so they can make thoughtful and reasonable decisions. This is our goal at MeaningCloud.