Development and Experimentation with Reasoning with Word Vectors in Julia

In this project you are going to develop your own word embedding utilities in Julia using APIs like

Word2Vector ([login to view URL]) and TextAnalytics

([login to view URL]). You need also to experiment with your developed utilities and

demonstrate its usefulness and accuracy. Your utilities need to be useful for in depth applications like:

 Compute similar words: Word embedding is used to suggest similar words to the word being subjected

to the prediction model. Along with that it also suggests dissimilar words, as well as most common

words.

 Create a group of related words: It is used for semantic grouping which will group things of similar

characteristic together and dissimilar far away.

 Feature for text classification: Text is mapped into arrays of vectors which is fed to the model for

training as well as prediction. Text-based classifier models cannot be trained on the string, so this will

convert the text into machine trainable form. Further its features of building semantic help in textbased classification.

 Document clustering is another application where word embedding is widely used

 Natural language processing: There are many applications where word embedding is useful and wins

over feature extraction phases such as parts of speech tagging, sentimental analysis, and syntactic

analysis.

With this project you will get experience and knowledge of word embedding and textual analytics in Julia.

Kĩ năng: Julia Language, Machine Learning (ML)

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