In the field of text analytics, aside from the development of categorization models, the application of machine learning (and more specifically, deep learning) has proved to be very helpful for supporting our teams in the process of building/improving rule-based models.
This post analyzes some of the applications of machine/deep learning for NLP tasks, beyond machine/deep learning itself, that are used to approach different scenarios in projects for our customers.
While it is obvious that the priority during this pandemic is to cure the sick, to prevent new cases from surfacing and to ensure there are economic and social measures in place to help the people and businesses most afflicted overcome the current situation; without a doubt, in the near future, the analysis of content related to the coronavirus that has been generated by the media and social network users will be the object of research for numerous disciplines such as sociology, philology, linguistics, audio-visual communication, and politics, to name a few.
At MeaningCloud we want to do our bit in this area, by applying our experience and our Text Analytics solutions to analyze the enormous volume of information in natural language, in Spanish and in other languages, in Spain and in other countries, given that, unfortunately, this is a global crisis.
This first article in the series centers on the thematic analysis of content that has been generated in Spanish by digital media platforms in Spain over the last month, how it has evolved during this period of time and the informative positioning of the main media platforms in Spain.
These other articles (only available, at the moment, in Spanish) analyse conversation topics on Twitter in Spain (both from the hashtags and general topics perspective and also applying a specific thematic categorization) and the linguistic analysis of presidential speeches related to this crisis.
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.
Pharmaceutical companies are extending their Voice of the Patient projects to include social media: comments on web forums, surveys, Twitter, and more.
The goal of the proof of concept ordered by one particular pharmaceutical company in Spain was to: ” Collect and analyze the voice of the patient, both quantitatively and qualitatively, from the channels where it is expressed”, including social networks like web forums, Facebook, Twitter, and other systems.
For the pharma industry, it is essential to listen and understand the feedback that their current and potential customers communicate through various means and touchpoints.
Web forums, for instance, gather millions of posts, and function as a meeting point for patients where support, experiences, and wisdom are shared with peers, family members, and friends.
MeaningCloud and University of Jaen have been the organizers of TASS, the Workshop on Semantic Analysis in Spanish language at SEPLN (International Conference of the Spanish Society for Natural Language Processing), again in 2018.
During the years, the research has extended to other tasks related to the processing of the semantics of texts that attempt to further improve natural language understanding systems. Apart from sentiment analysis, other tasks attracting the interest of the research community are stance classification, negation handling, rumor identification, fake news identification, open information extraction, argumentation mining, classification of semantic relations, and question answering of non-factoid questions, to name a few.
TASS 2018 was the 7th event of the series and was held in conjunction with the 34rd International Conference of the Spanish Society for Natural Language Processing, in Seville (Spain), on September 18th, 2018. Four research tasks were proposed. MeaningCloud sponsored this edition with prizes for the best systems in each of the tasks. A comprehensive description paper is (to be) published in Procesamiento del Lenguaje Natural journal, vol 62: TASS 2018: The Strength of Deep Learning in Language Understanding Tasks.
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
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
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.
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.
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.
Many users who want to incorporate text analytics into their Voice of the Employee (VoE) analysis aren’t sure how to translate their business requirements into something compatible with their current processes.
2024 © All Rights Reserved | Data protection policy | Terms and conditions MeaningCloud is a trademark by MeaningCloud LLC