Category Archives: Technologies

Technologies

NLP technologies: state of the art, trends and challenges

This post presents MeaningCloud’s vision on the state of Natural Language Processing technology by the end of 2019, based on our work with customers and research projects.

NLP technology has practically achieved human quality (or even better) in many different tasks, mainly based on advances in machine learning/deep learning techniques, which allow to make use of large sets of training data to build language models, but also due to the improvement in core text processing engines and the availability of semantic knowledge databases.

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How Artificial Intelligence makes RPA smarter: two use cases

RPA-automation-computer-robot-tools and statistics

Artificial Intelligence and RPA

Many organizations could be gaining huge operational efficiencies if they combined Artificial Intelligence and RPA (Robotic Process Automation).

In a previous post (The leading role of Natural Language Processing in Robotic Process Automation) we introduced the subject of NLP in RPA. In this post, we are seeing two use cases where Natural Language Processing (also known as Text Analytics) integrated with RPA/BPM software suites, is mature enough to solve typical insight extraction problems, conveniently and cost-effectively.

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Recorded webinar: Solve the most wicked text categorization problems

Thank you all for your interest in our webinar “A new tool for solving wicked text categorization problems” that we delivered last June 19th, where we explained how to use our Deep Categorization customization tool to cope with text classification scenarios where traditional machine learning technologies present limitations.

During the session we covered these items:

  • Developing categorization models in the real world
  • Categorization based on pure machine learning
  • Deep Categorization API. Pre-defined models and vertical packs
  • The new Deep Categorization Customization Tool. Semantic rule language
  • Case Study: development of a categorization model
  • Deep Categorization – Text Classification. When to use one or the other
  • Agile model development process. Combination with machine learning

IMPORTANT: this article is a tutorial based on the demonstration that we delived and that includes the data to analyze and the results of the analysis.

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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The leading role of NLP in Robotic Process Automation

RPA

Robotic Process Automation

Robotic Process Automation is gaining traction

Robotic Process Automation (RPA) has attracted considerable attention as a way to automate repetitive clerical tasks, by mimicking the way human workers carry them out. Since the introduction of the term (around the year 2000), RPA has evolved from simple screen scraping and desktop automation to the promise of Cognitive RPA. Reports by industry analysis leaders estimate the global spending on RPA software to reach $2.4B in 2022, with annual growth rates over 50%.

While the RoI of these investments is quite apparent, most analysts also stress that automation does not necessarily imply intelligence. In a recent article published by Forbes (“Sorry, but your bots are stupid”), Ron Schmelzer stresses the fact that automation is inherently dumb, and that automated software bots are still dumb. Concluding that “despite much of the marketing hype, what is being sold as intelligent automation is far from intelligent.”

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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


Our experience on Adverse Drug Reactions (ADR) identification at TAC2017

MeaningCloud and LaBDA research group were present at the TAC 2017 conference held on November 13th – 14th at NIST headquarters in Washington. In the Text Analysis Conferences, research groups from all over the world were invited to develop software systems to tackle text analytics-related problems. This year, one task was devoted to the automatic identification of adverse drug reactions (ADRs) appearing in drug labels, including features defining the ADR, such as its severity or if it is characteristic of a drug class instead of just a given drug. There has been a specific subtask to link the identified ADRs with their corresponding MedDRA codes and lexical terms. More than 10 research teams have taken part in the project, all of them applying some kind of deep learning approach to the problem. Results show that it is possible to reach 85% accuracy when identifying ADRs.

We were delighted to present our text analytics-based system for ADRs identification on drug labels, which combines natural language processing and machine learning algorithms. The system has been built as a joint effort between MeaningCloud and LaBDA research group at the Universidad Carlos III de Madrid. Identifying ADRs is a basic task for pharmacovigilance, and that is the reason why the Federal Drug Administration (FDA) is involved in the funding and definition of the ADRs identification tasks in the framework of the Text Analysis Conferences. We have learned a lot these days (e.g., a BiLSTM deep neural network is the best choice for the purpose), and shared pleasant moments with our colleagues at Washington. We hope to be able to attend next year’s edition, which will focus on the extraction of drug-drug interactions (DDI), another interesting task aimed at detecting situations where the use of a combination of drugs may lead to an adverse effect.


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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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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