Lead Semantics Selects Fluree for TextDistil Natural Language Processing Pipeline

In some cases, these errors can be glaring—or even catastrophic. For example, when Facebook was about to become a publicly listed company in 2012, how was it that company that was earning $1 billion in revenue was given a market valuation of $90 billion? The reason for this was based on several qualitative factors such as its core ideas, its teams, and its projected potential to earn high revenue, etc. In this view, qualitative data plays an important role even in financial decisions. In fact, structured quantitative data in the form of spreadsheets and relational databases only account for 20% of all available data. The remaining 80% is in the form of social media posts (especially Twitter), images, email, text messages, audio files, Word documents, PDFs and other unstructured forms.

In rigorous testing, AtScale’s integrated solution outperformed traditional methods by a wide margin. Across a diverse set of 40 business-related questions, the solution achieved a 92.5% accuracy rate, compared to just 20% for systems without the Semantic Layer. These results underscore the system’s capability to handle a wide range of query complexities with superior precision. Another issue is ownership of content—especially when copyrighted material is fed into the deep learning model.

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To many people, what distinguish machines from humans is emotion. True, some existentialists might push the envelope and go so far as to say consciousness (which is a valid argument), but the primary existential reality is emotion. A computer is not a living entity, does not understand empathy and cannot gauge how we feel. It does not and cannot care whether its users are happy, sad, frustrated or simply regretting a heavy lunch.

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A head shake can mean agree, disagree, confused, or simply a stiff neck. Sentiment thus plays a very important role in decision making and the ability of a machine to convert human language into machine readable code and convert it into actionable insights is the capability offered by NLP. In our previous research, we have largely focused on the quantitative methods of analysis. While quantitative data is easier to compartmentalize in the form of, say, share prices, time-series data analysis, qualitative data is harder to define and statistically model. NLP will also lead to more advanced analysis of medical data.

Lead Semantics develops and markets TextDistil, a Rule based Neural Language Pipeline that automates end-to-end extraction of Knowledge and Information buried in Text assets in the enterprise. Emotions and language, which are one of the ways in which we convey emotions, thus play an important role in the way investment decisions are executed. Owing to this, considerable progress has been made in recent years in understanding the subtleties of language and emotion.

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• Another issue is ownership of content—especially when copyrighted material is fed into the deep learning model.
• A head shake can mean agree, disagree, confused, or simply a stiff neck.
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• With these developments, deep learning systems were able to digest massive volumes of text and other data and process it using far more advanced language modeling methods.

In addition, some organizations build their own proprietary models. Early NLP systems relied on hard coded rules, dictionary lookups and statistical methods to do their work. Eventually, machine learning automated tasks while improving results. With these developments, deep learning systems were able to digest massive volumes of text and other data and process it using far more advanced language modeling methods. The resulting algorithms had become far more accurate and utilitarian.

The OpenAI codex can generate entire documents, based a basic request. This makes it possible to generate poems, articles and other text. Open AI’s DALL-E 2 generates photorealistic images and art through natural language input.

For example, a doctor might input patient symptoms and a database using NLP would cross-check them with the latest medical literature. Or a consumer might visit a travel site and say where she wants to go on vacation and what she wants to do. The site would then deliver highly customized suggestions and recommendations, based on data from past trips and saved preferences. NLP has revolutionized interactions between businesses in different countries. While the need for translators hasn’t disappeared, it’s now easy to convert documents from one language to another. This has simplified interactions and business processes for global companies while simplifying global trade.