I would like to classify abbreviations using machine learning. For example: I have watermel. and I ask for user what is watermel.(my application context is about food). Then He classify as watermelon. In other time, If Other user insert waterme. Is It exist a way to infer that waterme is the same as watermelon using machine learning techniques?
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$\begingroup$ You could use Machine Learning in general to solve this problem, but I feel your problem will have a more elegant solution in NLP. Please check that out first. Things like semantic analysis etc $\endgroup$– Rahul AedulaApr 13, 2017 at 14:35
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2 Answers
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Look into this package for Python: https://pypi.python.org/pypi/Distance/
You can use this to generate a numeric value representing the similarity between word.
Here is a similar post that should help: https://stats.stackexchange.com/questions/123060/clustering-a-long-list-of-strings-words-into-similarity-groups
Additionally, a level up in complexity would be to use t-SNE on an array generated using word2vec (this is word embedding). Examples and resources for this are: https://www.codeproject.com/tips/788739/visualization-of-high-dimensional-data-using-t-sne http://sebastianruder.com/word-embeddings-1/
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$\begingroup$ Thaks for these references. I'll learn study about it. $\endgroup$ Apr 16, 2017 at 14:15
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Natural Language Processing/Text Mining may help you to find out a possible solution to your problem. Because, in your case, it is inevitable to handle them with lexical parsers and analysers.
Hope this may helps!!
