The online service will compute a list of words that have been found in the text and categorize them in multiple ways. The words are for example sorted by relevancy and subject. A click on any word will also display the relationship to other words in the text, a definition of the word and example usage from the text.

A word cloud is displayed that is showing all words that have been discovered in the text. It is possible to filter words by relevancy or subject which can reduce the amount of words quite a bit. The subject filter is interesting as it displays word that fall into subjects such as math, science or art.

VocabGrabber has been designed to create a vocabulary list which can be a great help to understand and learn the text that has been pasted into the web form. It is currently only working with text that has been written in English.

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It’s a basic trainer that displays a word and requests the answer to be typed into the answer box. The next page will display the answer of the vocabulary trainer and your answer so that it is easy to compare the result. Statistics can be enabled that display how many words have been correctly answered so far.

One option that is well done is to flip languages on the fly. Instead of translating Latin to German it is also possible to translate German to Latin. The author provides three vocabulary sets on his website, German-Esperanto, German-Dutch and German-Latin. The format of the files is relatively straightforward and it should not take long to create your own data sets.

The format of the vocabulary sets is always the same, it begins with the name in the first line (#Name) and lists all words and their translations in the other lines. The word(s) and their translations are separated by a “_” sign. The line for the English word car and the German translation Auto would look like this: car_auto. If several words have the same meaning they can be added with the “;”character. car;automobile_auto;automobile.

The main problem that most users will face is that VocabOne does not provide enough vocabulary sets which means that they have to be created before the user can use the vocabulary trainer.

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