Auto Indexing with ML Architecture and Best Practices - XenonStack

What is Auto Indexing with Machine Learning?

The process of sorting and designating the terms related to index without any interference of human individual. This whole process includes different techniques algorithms, rulesets, Natural Language Processing and when there is a task of automation, Machine Learning is "go to" technique for sure. The era solely dedicated to Artificial Intelligence. Not only private limited firms but government firms also adapting automation to some extent. Every automation requires Machine Learning in the core because Machine Learning is the technique to train a computer toward a specific goal using data.


How Auto Indexing with Machine Learning Works?

Automation requires learning of machine or training of device directly associated with the use of Machine learning. But Machine learning is itself a forest of newly emerging techniques from which the choosing the right fruit solely depends on the use case.

Different phases of the process involved –

  • Database and its metadata with information.
  • Recognizing the indexes of entities.
  • Machine Learning techniques.
  • Recommendation for index and generation.
  • Optimizer for the process of indexing.
  • Suggestion of Optimizer.

Benefits of Auto Indexing with Machine Learning

  • The method of producing Index becomes swift and smooth.
  • Modification becomes smooth.
  • Automation in Indexing supports transferability.
  • Improves time complexity in regard to resources.
  • Reduction in usage of resources.
  • Enhance the accuracy of the indexing process.
  • Reduce the load of extra application and databases, reduce duplicity of configuration.
  • Accelerate the importing process of data and documents.

Why Auto Indexing with Machine Learning Matters?

Indexing is a vital part for storing documents as it saves time and the costs for searching and sorting documents. Why not manual indexing, Why Automate Indexing? The reason is simple automation is speedy and cost-effective.

The second reason is Data is not increasing linearly, it is expanding exponentially not only for Indexing, this increment increasing difficulty for all manual processes. That is why automation is also a need of changing time.

So many software available in the market based on automation. The examples of this software are Adobe Framemaker, Extract and Microsoft Word. These software outcast other software which supports manual indexing in terms time complexity as well as regarding simplicity.

Automation Indexing used for classifying the unstructured documents into the specific templates. These techniques used for converting unstructured documents to well-defined structures.


How to Adopt Auto Indexing with Machine Learning?

When there is a need of model which works with text data, Pre-processing plays a crucial role and in the case of Automated Indexing, pre-processing includes Index detection, Tokenization, Removal of stop words and stemming. NLTK library can be used to accomplish these tasks.

Every use case considered different. And there is a need to select the proper Machine Learning technique for a specific use case. In the case of text data some of the Machine Learning techniques are Multinomial Naive Bayes, Support Vector Machine (Classification), Random Forests and in the case of Unsupervised Learning, accomplished using different clustering technique.

Word Embedding is a crucial part of the whole procedure to give semantic meaning to each word separately.


Best Practises of Auto Indexing with Machine Learning

  • Give particular concern to all tasks of Pre-processing.
  • Selection of Proper Machine learning technique is a must.
  • It is not necessary that only one type of Machine learning technique sufficient for implementing the whole procedure of automation. There can be requirements for using different Machine Learning techniques for accomplishing different subtasks of the entire procedure.
  • After training the model, the model tested and appropriately validated using different Machine Learning testing and Validating techniques.
  • Optimize the model to get better results, is also an unavoidable sub-task of the whole procedure.

Tools for Auto Indexing with Machine Learning

TypeTools
Fully functional Automated Indexing softwareMicrosoft Word, Adobe Framework and Extract
Machine Learning Techniques used for ModelingDeep learning Algorithms = Recurrent Neural Networks, Long Short-Term Memory (LSTM). Machine Learning Algorithms = Multinomial Naive Bayes, Support Vector Machine (Classification), Random Forests
Libraries usedTensorflow, Keras, MXNet, Scikit, NLTK