Category Archives: Text Analytics

Post that discuss text analytics technology.

Text analytics explained: MeaningCloud in Italian

In previous posts we spoke about text analysis performed in French and Portuguese. Today we’re wrapping up this linguistics series by discussing the analyses that can be done with Italian texts.

Italian is spoken in several European countries such as Italy, San Marino and Switzerland, totaling almost 70 million speakers. As Italians have migrated all over the world, its language is also present on the other side of the pond. In South America, for instance, it is the second most spoken language in Argentina. In the US, even though it is not an officially spoken language, many of its citizens are of Italian descendent and thus speak the language at home. We wanted to include such a widely spread language in our Standard Languages Pack.

Hello in many languages

Similarly to our previous posts, we are going to explain, in a linguistically-inclined way, what Text Analytics is and which functionalities MeaningCloud provides in Italian.

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TASS 2018: Fostering Research on Semantic Analysis in Spanish

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.

TASS logo

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.

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Text analytics explained: MeaningCloud in Portuguese

A few weeks ago we talked about MeaningCloud’s text analytics performance on French texts. Now it’s Portuguese time!

Portuguese, together with Spanish, has an enormous presence in South America. It is spoken by more than 200 million people in Brazil alone. Not only does it have an immense influence on the economy in South America but throughout Europe too, where it is used by more than 10 million speakers. Africa also has Portuguese-speakers. Angola, which has a population of more than 24 million people, recognizes Portuguese as their official language. Its presence in these three continents makes it hard to miss in our Standard Languages Pack. At MeaningCloud, we offer two Portuguese varieties: Brazilian Portuguese and European Portuguese.

Hello in many languages

Whether the concept “Text Analytics” sounds rather hazy or you are looking for something more specifically language-related, this post is for you. We keep in mind the language diversity and we want to show you all the functionalities we provide in Portuguese.

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Text analytics explained: MeaningCloud in French

Due to the rise of Natural Language Processing technologies, Text Analytics is on everyone’s lips. However, most services in this field are provided in English and, depending on the language you are interested in, it can become difficult to find the functionality you are looking for.

No worries. French, for instance, is a language not only used in all the five continents and with almost 300 million of speakers, but is also either the first or the second language of communication in many international organizations [1]. No wonder why we have it as a part of our Standard Languages Pack!

Hello in many languages

Whether the concept “Text Analytics” sounds rather hazy or you are looking for something more specifically language-related, this post is for you. We keep in mind the language diversity and we want to show you all the functionalities we provide in French.

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MeaningCloud sponsors the award for Author Profiling Research at PAN also in 2018

Author Profiling and Text Forensics Research

CLEF Conference 2018Since 2009 the PAN Lab organizes shared tasks on digital text forensics in general, and in author profiling in particular. Pan Lab is part of CLEF, the European Conference and Evaluation Forum around Information Retrieval. CLEF consists of an independent peer-reviewed conference on a broad range of issues in the field of multilingual and multimodal information access evaluation, and a set of labs and workshops designed to test different aspects of mono and cross-language information retrieval systems. CLEF 2018 will be hosted by the University of Avignon, France, 10-14 September 2018.

MeaningCloud has been sponsoring the award to the best performing team in the author profiling task at CLEF since 2015.

Author profiling is a task that given a document has the aim to infer what are the traits of its author.
In 2017 the task focused on gender and language variety identification in Twitter addressing four languages and several of their varieties: English (Australia, Canada, Great Britain, Ireland, New Zealand, United States), Spanish (Argentina, Chile, Colombia, Mexico, Peru, Spain, Venezuela), Portuguese (Brazil, Portugal), and Arabic (Egypt, Gulf, Levantine, Maghrebi).

Paolo Rosso delivers the 2017 PAN Author Profiling Price to the team of University of Groningen

Paolo Rosso delivers the 2017 PAN Price to the team of University of Groningen

Twenty-two were the participating teams from all over the world in 2017 and the best results were obtained by Angelo Basile, Gareth Dwyer, Maria Medvedeva, Josine Rawee, Hessel Haagsma, and Malvina Nissim, from the University of Groningen, The Netherlands.

This year the task will go multimodal and not only textual information in tweets will be taken into account but also images of URLs will be used as information sources in order to infer gender demographics. Three will be the languages that will be addressed: English, Spanish and Arabic [http://pan.webis.de/clef18/pan18-web/author-profiling.html].

Paolo Rosso
Universitat Politècnica de València, Spain
Co-organizer of the author profiling task at PAN

References

Rangel F., Rosso P., Potthast M., Stein B. (2017). Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter. In: Cappellato L., Ferro N., Goeuriot L, Mandl T. (Eds.) CLEF 2017 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1866. [http://ceur-ws.org/Vol-1866/invited_paper_11.pdf]

Potthast M., Rangel F., Tschuggnall M., Stamatatos E., Rosso P., Stein B. (2017). Overview of PAN’17: Author Identification, Author Profiling, and Author Obfuscation. In: 8th Int. Conf. of CLEF on Experimental IR Meets Multilinguality, Multimodality, and Visualization, CLEF 2017,
Springer-Verlag, LNCS(10456), pp. 275–290 [http://www.uni-weimar.de/medien/webis/publications/papers/stein_2017k.pdf]


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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Voice of the Employee Dashboard

Voice of the Employee gathers the needs, wishes, hopes, and preferences of all employees within an organization. The VoE takes into account both explicit needs, such as salaries, career, health, and retirement, as well as tacit needs such as job satisfaction and the respect of co-workers and supervisors. This post follows the line of Voice of the Customer in Excel: creating a dashboard. We are creating another dashboard, this time for the Voice of the Employee.

Text-based data sources are a key factor for any organization that wants to understand the “whys”.

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