Disclaimer: The author is not affiliated with IKEA. The results shown here are intended for educational and entertainment purposes only and do not imply endorsement.
I had wanted to experiment with natural language processing using LSTM networks for a while, when I saw some discussion on the social media regarding the quirky names of IKEA furniture. I realized that they had a few thousand products, which should be enough to train a simple network to generate completely made up product names.
Fortunately the hard work of data collecting had already been done for me: I mined a dataset of product names from the IKEA dictionary. I then wrote some processing code in Python and implemented the LSTM model in Keras.
To see how the model is run, check out the code and this Jupyter notebook that gives an example of using it.
You can also clone the code and use the command line interface. Running this in the ikeanames folder should generate 100 names with the pre-trained model included with the code:
python ikeanames.py generate 100
Here are one examples output of generated names:
GÅNGAL KORT* TABMAN TOMMEN SORSA
GÄSJÖ TITTADIA UTBY* ILIOMA LEDANA
GÖTA PRAKTIT PRASKE BILJARL PRUMÖR
STULD MAJKÖ VARRELUDDA ARNÖM TÄLLENG
BOLSVAT RADERA KAAMBY JONNE BLADA
REMONH ANGELF MÅNGEN BERNNET INNARIS
SÄLLAR EFBY TRULS* LAGNI SKRÄCKÖ
BERENN JYGGJAA MIRGA EDRIKMÄN INDAL
LJULDAN GYSING BEVJÄT TRYPS FLYK
SNÖRS MARAK VID BELA LONDED
KOJE INDO* GRÖKIG KNASS BERRSKAM
VOFTER ESKME TÄLLHOLM RÖNE SKODVEN
GRÄSÖR SSÖRLIN TULLA FLABO LURGER
KRUTT MORA HALLCEHYLL DANTA KLEVBAR
FENDIK TOLLA TROSURDA IMEBAL SKÄMDA
BORK TROMMEN ÖFJA ORGELFIKÄR GRÖNA
LASTAN ENBAR GAMOLIS DRIGG KORDENELL
UMMEREN FRANKLIG ONRELS SERJÖ BIMÅS
JOVERR BARRKA SANE ÅNARLUS BEDUDDER
KORPETIN ESTER EKIS SLÄNG PLÄL
The results are quite interesting: The model has clearly learned enough about the relationship of letters in Scandinavian languages to be able to generate halfway decent Scandinavian-sounding Pig Latin. A Swedish speaker should have no problem pronouncing most of these words even if most of them are just nonsense. The names ending with an asterisk are ones that were present in the training set. Clearly the model does much more than just repeat examples from its training. It actually manages to generate some words that do mean something even if they're not in the training set, for example "Göta" and "Gröna".