A recommendation/search Engine using Elasticsearch

Tanu91


19

Votes

Description:

ElasticSearch is a search engine and an analytics platform. But it offers many features that are useful for standard Natural Language Processing and Text Mining tasks. It incorporates internally some very basic tools from the NLP domain such as tokenization and stemming.

Analysis is the secret sauce in elasticsearch’s ability to deal with natural language and other complex data. The analyze API can be used to test an analysis process can be extremely helpful when tracking down how information is being stored in your Elasticsearch indices. This API allows you to send any text to Elasticsearch, specifying what analyzer, tokenizer, or token filters to use, and get back the analyzed tokens. That is very often all you need for preprocessing for higher level tasks such as Machine Learning, Language Modelling etc.

What talk covers:

. Use of Elasticsearch in Text Mining & Basic NLP techniques.
. Exploring OpenLibrary books data with Elasticsearch.
. Relevant queries for searching books.
. Integration of Books recommendation/search engine with Facebook Messenger with help of Django.

Prerequisites:

Attendees should have a basic knowledge of:

1) Working with Python Modules

2) Basics understanding of Text Mining.

3) Facebook Messenger Bot Platform.

Section: Others
Type: Talks
Target Audience: Intermediate
Last Updated:

Hi! Could you please add up slides to your talk/workshop?

Shivani Bhardwaj (~shivan1b)

Hi! The selection process will begin by 4th, It'll increase your chances if you add slides as soon as possible.

Shashank Kumar (~realslimshanky)

Sorry for the confusion. The selection process will be done by 4th.

Shashank Kumar (~realslimshanky)

@shivan1b @realslimshanky : I will share the slides link soon :)

Tanu91

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