Category Archives: MeaningCloud

This category groups the different aspects of MeaningCloud we talk about in the blog.

COVID-19 Crisis: doing our part

As our CEO, Jose Gonzalez, announced a couple of days ago, in MeaningCloud, we believe that every little helps, and so we are adopting some measures to help our users and clients in these trying times.

Starting now:

  • We will provide full access to anyone participating in any of the NLP-related tasks published by platforms such as Kaggle to help in the research to fight or analyze the impact of COVID-19. Just contact us at support.
  • The Start-Up plan is doubled in volume: all current subscriptions, as well as subscriptions created while the COVID-19 crisis, persists will allow up to 240k credits per month.
fight COVID-19
  • The Start-Up plan now comes with a discount of $25 over its usual price for the next 3 months:
    • The discount has already been applied to all our current Start-Up subscriptions.
    • Any new subscription may benefit from this discount adding the coupon COVID-STARTUP in the upgrade process.
  • All our packs now cost $99 for the next 3 months. This discount has already been applied to current subscriptions to packs. New subscriptions should use the following coupons on checkout depending on the pack:

The upgrade process should include only the item the coupon applies to. These coupons are valid until May 31, 2020, or until the crisis ends.

Stay safe and take care of each other.

The MeaningCloud team


Fighting the coronavirus together – Message from the CEO

Fighting COVID-19

Dear friends,

In these challenging times, caused by the severe impact of the coronavirus, we have considered how we can best help our customers, friends and the communities our employees are a part of.

First of all, we believe that continuing our daily work and meeting commitments for our customers is our best contribution to the global economy and the recovery that should ensue. Digital companies must continue to contribute to the generation of wealth at a time like this. The loss of employment and vast public spending caused by the pandemic, with incalculable costs in health, social, and business protection, morally force us to contribute to sustaining the economy from our technological trench.

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RapidMiner + Python + MeaningCloud = 🚀

Integrations with third-party software are something extremely useful: they allow you to use technology outside the tool you are using, giving you additional features outside its core functionality or just providing auxiliary tools to make your day to day easier.

One of the downsides is that you are limited by the functionality the integration provides. Usually, this is not much of a problem as standard integrations tend to cover the most common use cases, but in the case of tools that can be used in many scenarios, these uses cases may not be exactly what you need or want for your application.

MeaningCloud is not an exception to this. We provide many different APIs, each one of them with several types of analyses and with tons of possible applications. It’s not surprising that not all of them are included in MeaningCloud’s extension for RapidMiner.

mc+python+rm

If you want something like the global polarity Sentiment Analysis provides, then the extension for RapidMiner has you covered, but it may not be the case for other analyses. It can go from wanting to use a MeaningCloud API not included in the extension such as the Summarization API or to something as small as needing the label of the resulting categories in an automatic classification process instead of the code the extension provides.

Last year, RapidMiner published a new Python scripting extension: Execute Python. This operator allows you to run a Python script in RapidMiner, which enables you to include any processing you want and can code in a Python script in your RapidMiner process.

Using this new functionality and MeaningCloud’s Python SDK, we can create a Python script to use any of MeaningCloud APIs directly from RapidMiner. The SDK enables us to work with the API output easily and to extract whatever information we want to add to our RapidMiner processes.

Let’s see how we can do this! Continue reading


Emotion Recognition in MeaningCloud

In our previous post, we made an introduction to emotion recognition to celebrate the release of the publication of our Emotion Recognition pack. We talked about how emotions play a prominent role in the individual and social life of people and how they have a great impact on their behavior and judgements.

We also saw how thanks to Natural Language Processing we can extract the underlying emotions expressed in a text in a fast and simple way and we saw how useful it can be in multiple scenarios.

In this post, we are going to explain in depth how to get the most out of our emotion recognition pack. We will talk about the criteria and considerations we’ve followed in our approach.

Emotions

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الصيحة! Text Analytics in Arabic

At MeaningCloud we aim to provide the most advanced text analytics product with the broadest language coverage in the market. That’s why before we finished 2019 we worked on launching several new language packs to increase the coverage given by our standard pack — English, Spanish, French, Italian, Portuguese and Catalan — and our Nordic pack — Swedish, Danish, Norwegian and Finnish.

The third pack we launched is the Arabic pack. Arabic, the fifth most spoken language in the world, is the official language in twenty countries and co-official in six others. It is the first language of 280 million speakers, and the second language of another 250 million. Moreover, for religious reasons, several million Muslims living in other countries have knowledge of Arabic.

Its most peculiar characteristic is that it uses its own writing system, from right to left, joining the letters together. In this way, each letter can have up to four forms. It is also interesting that, despite the fact that they were introduced in the 1920s, there are no capital letters in Arabic. Since sometimes common names can be confused with proper names, the latter are usually enclosed in parentheses or quotes.

MeaningCloud now provides coverage for Arabic for the following functionality:

Arabic

This coverage will be extended through the successive product releases depending on the market demand. Find detailed information on our new language coverage page.

So, what are these text analytics tasks and what are they used for?
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Text Analytics for the Contact Center of the Future

The contact center is a crucial component of the customer experience and increasingly incorporates more channels based on unstructured information. In this post we analyze how advanced semantic analysis can be used to get the most out of the contact center of the future.

The Rise of the New Contact Center

Interest in the contact center has multiplied by its greater role as an essential component of the customer experience. New interaction channels (bots, chats, social) add to the traditional email and telephone and enable innovative ways to connect with clients in both inbound and outbound contact centers, both internal to companies of all types and in those operated by BPO vendors to provide outsourced services.

In this way, the contact center (traditionally known as call center) has ceased to be a cost center to become a tool for proactively communicating with and understanding the market, for multichannel business development and for generating value for the company.
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Ура! Text Analytics in Russian

At MeaningCloud we aim to provide the most advanced text analytics product with the broadest language coverage in the market. That’s why before we finished 2019 we worked on launching several new language packs to increase the coverage given by our standard pack — English, Spanish, French, Italian, Portuguese and Catalan — and our Nordic pack — Swedish, Danish, Norwegian and Finnish.

The second pack we launched is the Russian pack. Russian is the official language of the Russian Federation, Belarus, Kazakhstan and Kyrgyzstan. It was the de facto language in the Soviet Union, so its use it’s also common in the Baltic States, the Caucasus and Central Asia. It’s the most common of the Slavic languages with almost 144 million speakers.

Russian is written using the Cyrillic alphabet, and although transliteration into the Latin alphabet has been common due to the technical restrictions and to the unavailability of Cyrillic keyboards abroad, it’s used less and less thanks to the Unicode extension that incorporates the Russian alphabet and the many free programs that leverage it.

MeaningCloud now provides coverage for Russian for the following functionality:

Russian pack

This coverage will be extended through the successive product releases depending on the market demand. Find detailed information on our new language coverage page.

So, what are these text analytics tasks and what are they used for?
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好棒! Text Analytics in Chinese

At MeaningCloud we aim to provide the most advanced text analytics product with the broadest language coverage in the market. That’s why before we finish 2019 we have worked on launching several new language packs to increase the coverage given by our standard pack — English, Spanish, French, Italian, Portuguese and Catalan — and our Nordic pack — Swedish, Danish, Norwegian and Finnish.

The first pack we are launching is the Chinese pack. Chinese, the official language of the People’s Republic of China. It’s the language with the most native speakers, almost a 16% of the global population.

Chinese (in all its varieties) is a group of languages based on ideograms, traditionally arranged in vertical columns, read from top to bottom down a column and right to left across columns. The variety covered by MeaningCloud is simplified Chinese.

MeaningCloud now provides coverage for Chinese for the following functionality:

Chinese pack

This coverage will be extended through the successive product releases depending on the market demand. Find detailed information on our new language coverage page.

So, what are these text analytics tasks and what are they used for?
Continue reading


Performance Metrics for Text Categorization

One of the most common and extensively studied knowledge extraction task is text categorization. Frequently customers ask how we evaluate the quality of the output of our categorization models, especially in scenarios where each document may belong to several categories.

The idea is to be able to keep track of changes in the continuous improvement cycle of models and know if those changes have been for good or bad, to commit or reject them.

This post gives answer to this question describing the metrics that we commonly adopt for model quality assessment, depending on the categorization scenario that we are facing.

 

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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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