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WordPanda Tests Smart English Lessons

Real deep learning can be based on understanding how the language works. The deeper we understand the rules the language obeys, the less senseless memorizing we’ll need. But to share this understanding, we have to obtain it first.

Real deep learning can be based on absolute understanding how the abstract language works. But to use this knowledge properly we have to obtain it. In fact, does a giant job on analyzing real spoken language and its parts to offer the best for any word you’re searching for.

It’s a great picture even now. For whatever word you select, you get its explanations from various sourced and dictionaries, examples of its pronunciation in British, American, or Australian English, the most widely used synonyms, antonyms, rhymes, the words that starts with amount of the same letters, and even stats on how often this word has been used in literature. All this info can be saved as a PDF file, a complete dossier of the word. But there’s more to words, and WordPanda online dictionary is working on it.

More understanding, less repetitions—that’s the motto. The deeper we understand the principles the language obeys, the less senseless memorizing we’ll need. But to share all this understanding, we have to obtain it first.

And here are some methods WordPanda uses to get further in its studies.

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1. Big Data. These words have been used together a lot recently. Briefly, it means that analyzing large volumes of data can discover some relationships and correlations that don’t seem that obvious. Applying big data to language means analyzing really, really much texts, any types of them: fiction, scientific texts, news, articles, colloquial speech, private notes available in public access by social network users.

The results can be astonishing. For example, you can see a full story of any new, old, or obsolete word. The graphs show when this word was first noticed, how it popularity changed through history, when it faded or rose again. You can look for actual or more obsolete synonyms or connected words. For example, if you’re writing a book or a script and exploring the time of your action, you can provide your characters with the most authentic vocabulary of the time you describe.

2. Machine learning. 10-20 years-old computers couldn’t act without explicit programming. But today we can rely on machines that can compare large sets of the data and find out relations between different objects, and then use them in further work. This is machine learning. Our main goal is to gather information on any certain word and then present the most important part of it in brief. That is, what the words really means, the most common expressions with it, synonyms, related forms of the words. Gathering this data requires a lot of work, and the criteria are not always linear. With machine learning gathering and processing all this data is much more effective and faster.

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3. Multi-user systems. plans to personalize user experience by providing special settings based on user’s behavior. These features will be added later. For now you can log in with your Facebook credentials (or through other social network account), but you’ll feel no advantage yet.

But it can change a bit later. Add your bookmarks, offer your own translation, create your subdictionaries, even write your own field noted. It all would be impossible with a printer dictionary (or at least would harm it, like handwritten notes). With a personalized account these enhancements would be your own. On the other hand, the system will use machine learning models and algorithms to offer the best experience for most common user cases.

Well, panda seems a slow animal, but if we have a whole forest of motivated pandas, they will soon explore any bamboo offspring and put it on the map. So we work with big data, having full English vocabulary processed by Word Panda.

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