ngram – Data Science, Data Analytics and Machine Learning Consulting in Koblenz Germany https://www.rene-pickhardt.de Extract knowledge from your data and be ahead of your competition Tue, 17 Jul 2018 12:12:43 +0000 en-US hourly 1 https://wordpress.org/?v=4.9.6 Paul Wagner and Till Speicher won State Competition "Jugend Forscht Hessen" and best Project award using neo4j https://www.rene-pickhardt.de/paul-wagner-and-till-speicher-won-state-competition-jugend-forscht-hessen-and-best-project-award-using-neo4j/ https://www.rene-pickhardt.de/paul-wagner-and-till-speicher-won-state-competition-jugend-forscht-hessen-and-best-project-award-using-neo4j/#comments Fri, 16 Mar 2012 11:18:38 +0000 http://www.rene-pickhardt.de/?p=1204 6 months of hard coding and supervising by me are over and end with a huge success! After analyzing 80 GB of Google ngrams data Paul and Till put them to a neo4j graph data base in order to make predictions for fast scentence completion. Today was the award ceremony and the two students from Darmstadt and Saarbrücken (respectivly) won the first place. Additionally the received the “beste schöpferische Arbeit” award. Which is the award for the best project in the entire competition (over all disciplines).
With their technology and the almost finnished android app typing will be revolutionized! While typing a scentence they are able to predict the next word with a recall of 67% creating a huge additional vallue for today’s smartphones.
So stay tuned of the upcomming news and the federal competition on May in Erfurt.
Have a look at their website where you can find the (still) German Documentation. As well as the source code and a demo (which I also include here (use tab completion (-: as in unix bash)
Right now it only works for German Language – since only German data was processed – so try sentences like

  • “Warum ist die Banane krumm” (where the rare word krumm is correctly predicted due to the relation of the famous question why is the banana curved?
  • “Das kann ich doch auch” (I am also able to do that)
  • “geht wirklich nur deutsche Sprache ?” (Is really only German language possible?)


<br /> Ihr Browser kann leider keine eingebetteten Frames anzeigen:<br /> Sie können die eingebettete Seite über den folgenden Verweis<br /> aufrufen: <a href=”http://complet.typology.de” mce_href=”http://complet.typology.de” data-mce-href=”http://complet.typology.de”>Demo</a><br />

]]> https://www.rene-pickhardt.de/paul-wagner-and-till-speicher-won-state-competition-jugend-forscht-hessen-and-best-project-award-using-neo4j/feed/ 11 Download Google n gram data set and neo4j source code for storing it https://www.rene-pickhardt.de/download-google-n-gram-data-set-and-neo4j-source-code-for-storing-it/ https://www.rene-pickhardt.de/download-google-n-gram-data-set-and-neo4j-source-code-for-storing-it/#comments Sun, 27 Nov 2011 13:28:20 +0000 http://www.rene-pickhardt.de/?p=840 In the end of September I discovered an amazing data set which is provided by Google! It is called the Google n gram data set. Even thogh the english wikipedia article about ngrams needs some clen up it explains nicely what an ngram is.
http://en.wikipedia.org/wiki/N-gram
The data set is available in several languages and I am sure it is very useful for many tasks in web retrieval, data mining, information retrieval and natural language processing.
This data set is very well described on the official google n gram page which I also include as an iframe directly here on my blog.

So let me rather talk about some possible applications with this source of pure gold:
I forwarded this data set to two high school students which I was teaching last summer at the dsa. Now they are working on a project for a German student competition. They are using the n-grams and neo4j to predict sentences and help people to improve typing.
The idea is that once a user has started to type a sentence statistics about the n-grams can be used to semantically and syntactically correctly predict what the next word will be and in this way increase the speed of typing by making suggestions to the user. This will be in particular usefull with all these mobile devices where typing is really annoying.
You can find some source code of the newer version at: https://github.com/renepickhardt/typology/tree/develop
Note that this is just a primitive algorithm to process the ngrams and store the information in a neo4j graph data base. Interestingly it can already produce decent recommendations and it uses less storage space than the ngrams dataset since the graph format is much more natural (and also due to the fact that we did not store all of the data saved in the ngrams to neo4j e.g. n-grams of different years have been aggregated.)
From what I know the roadmap is very clear now. Normalize the weights and for prediction use a weighed sum of all different kinds of n-grams and use machine learning (supervised learning) to learn those weights. As a training data set a corpus from different domains could be used (e.g. wikipedia corpus as a general purpose corpus or a corpus of a certain domain for a special porpus)
If you have any suggestions to the work the students did and their approach using graph data bases and neo4j to process and store ngrams as well as predicting sentences feel free to join the discussion right here!

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