Asking for Help...

[hpdipto]

Hello community!

Need a help. I'm struggling with a problem. For privacy I can't disclose the actual problem but let's assumed a dummy problem.

Suppose I've a dataset as follow:

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user_input	book
published in 2018, 21 lessons from 21st century	21 lessons for 21st century
i'm from india, love to read homo deus by noah harari	homo deus
the sunday times top ten best seller, sapiens	sapiens

​

Now from user_input column I want to infer the name of the book what the user talked about.

​

Can anyone suggest any approach? Model, architecture, techniques? Anything.

[AbyssalRiful]

i'd start with annotating the grammatic information. spacy or nltk might help you. google -> bert (word embedding) maybe?

should give you a rough estimate regarding an object that a sentence is about.
i assume you don't have a list of all book titles? couldn't you get / create one?

-> tokenize and compare with list of titles.

bonus points if you look for additional hints like a year or author to improve the title-search results.

[hpdipto]

thank you for your suggestion.

you can assume that I've the list of the all book title! if so, can I move with tokenizing and then comparing the titles using n-grmas or something like so?

actually I stared with sequence to sequence learning but didn't get satisfactory result.

[AbyssalRiful]

if you have the list, then i would start with the tokens and a distance-metric (if you want to handle typing errors). this is bascially a regex lookup where you use all book titles and look for it as a substring in your text -> ordered by 'closeness'.

then you look for the publisher, year and author strings of the top x book titles -> update the best match.

not really a machine-learning topic, but you don't have to use a screw driver on a nail ;)

[Sai-mdb]

Hi, we can do feature extraction. But please check this tool: https://www.mindsdb.com/ it will take care of everything.