Category Archives: Semantic Processing

Posts about the semantic processing.

MeaningCloud participates in the first Global Legal Hackathon

global legal hackaton

The first phase of the first Global Legal Hackathon (GLH) was held February 23-25, 2018. David Fisher, organizer of the event and founder of the technological and legal company Integra Ledger, estimates that the GLH will have a great impact. He hasn’t spoken too soon; global participation in the GLH nearly matched that of an earlier event organized by NASA, and it has been considered the largest hackathon organized to date. For 54 hours, more than 40 cities across six continents participated simultaneously. The teams were made up of engineers, jurists, lawyers, and people in business who all worked toward a common goal: to lay the foundations for legal projects that can improve legal work or access to legal information through an app, program, or software. Continue reading


Applying text analytics to financial compliance

In one of our previous posts we talked about Financial Compliance, FinTech and its relation to Text Analytics. We also showed the need for normalized facts for mining text in search of suspects of financial crimes and proposed the form SVO (subject, verb, object) to do so.

financial crime

Financial crime

Thus, we had defined clause as the string within the sentence capable to convey an autonomous fact. Finally, we had explained how to integrate with the Lemmatization, PoS and Parsing API in order to get a fully syntactic and semantic enriched JSON-formatted tree for input text, from which we will work extracting SVO clauses.

In this post, we are going to continue with the extraction process, seeing in detail how to work to extract those clauses from the response returned by the Parsing API.

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How to build a Financial Compliance model ready for FinTech

What is Financial Compliance and what is FinTech?

financial crime

Financial crime

Financial crime has increasingly become of concern to governments throughout the world. The emergence of vast regulatory environments furthered the degree of compliance expected even from other non-governmental organizations that conduct financial transactions with consumers, including credit card companies, banks, credit unions, payday loan companies, and mortgage companies.

Technology has helped financial services address the increased burden of compliance in innovative ways which have also yielded other benefits, including improved decision-making, better risk management, and an enhanced user experience for the consumer or investor.

The rapid development and employment of AI (Artificial Intelligence) techniques within this specific domain have the potential to transform the financial services industry.

FinTech (Financial Technology) solutions have recently arised as the new applications, processes, products, or business models in the financial services industry, composed of one or more complementary financial services and provided as an end-to-end process via the Internet. You can find additional interesting information in this article.

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MeaningCloud’s Artificial Intelligence at EyeforPharma

Robotick hand touches ipad

Text-based Artificial Intelligence for the Pharma Industry

At MeaningCloud, we are proud to sponsor the Eye for Pharma Conference. Data, Evidence and Access Summit 2017. November 13-14th, 2017 – Philadelphia, US. MeaningCloud’s value proposition for the conference can be summarized as Text-Based Information with Artificial Intelligence.

Eye for Pharma is about demonstrating and communicating value, no matter which department you’re in. Whether it’s exploring innovative uses of real-world evidence (RWE) or creating new outcomes-based pricing models, only by embracing the power of data can you fully unlock the value of your drugs. It is a great opportunity for learning and networking.

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Voice of the Customer in the insurance industry

For insurance companies, it is vital to listen and understand the feedback that their current and potential customers express through all kinds of channels and touch points. All this valuable information is known as the Voice of the Customer.  By the way, we had already dedicated a blog post to Text mining in the Insurance industry.

(This post is a based upon the presentation given by Meaning Cloud at the First Congress of Big Data in the Spanish Insurance Industry organized by ICEA. We have embedded our PPT below).  

More and more insurance companies have come to realize that, as achieving product differentiation at the industry is not easy at all, succeeding takes getting satisfied customers.

Listening, understanding and acting on what customers are telling us about their experience with our company is directly related to improving the user experience and, as a result, the profitability. In the post on Voice of the Customer and NPS, we saw in more detail this correlation between customer experience and benefits.

 

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An Introduction to Sentiment Analysis (Opinion Mining)

In the last decade, sentiment analysis (SA), also known as opinion mining, has attracted an increasing interest. It is a hard challenge for language technologies, and achieving good results is much more difficult than some people think. The task of automatically classifying a text written in a natural language into a positive or negative feeling, opinion or subjectivity (Pang and Lee, 2008), is sometimes so complicated that even different human annotators disagree on the classification to be assigned to a given text. Personal interpretation by an individual is different from others, and this is also affected by cultural factors and each person’s experience. And the shorter the text, and the worse written, the more difficult the task becomes, as in the case of messages on social networks like Twitter or Facebook.

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What You Need To Know about Text Analytics

You have enough to worry about. You know your industry inside and out. You know your products and services and how they compare with the competition’s strengths and weaknesses. In business, you have to be an expert in a range of topics. What you don’t need to worry about are the ins and outs of every technology, algorithm and software program.

This is especially true of an inherently complex technology such as natural language processing. As a business owner you have enough to worry about. Do you really have time to understand morphological segmentation? Text analytics should be just another tool in your toolbox to achieve your business goals. The only thing you need to know is what problems you have that can be solved by natural language processing. Anaphoric referencing? Don’t worry about it. We have it covered it, along with anything else you might need from language technology.

Text Analytics

What do you do need to know about text analytics?

Text analytics goes by many names: natural language processing (NLP), text analysis, text mining, computational linguistics. There are shades of difference in these terms, but let the expert work that out. What you need to know is that these terms describe a variety of algorithms and technology that is able to process raw text written in a human language (natural language) to provide enriched text. That enrichment could mean a number of things:

  • Categorization – Classifying text according to themes, categories or a taxonomy
  • Topic Extraction – Identifying key named entities and concepts in the text such as people, places, organizations, and brands
  • Sentiment Analysis – Detecting whether the text is talking about those concepts in a positive or negative light

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