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1. NaturalText Technology

2. Text Analytics

3. Examples

4. Assumptions Made for Financial Sector data

5. Training Algorithm for specific tasks

6. Cloud Deployment and Data Protection

7. About NaturalText

1. NaturalText Technology

NaturalText applications based on Unsupervised Deep Learning Algorithm that can auto-magically learn from input data. It can use seed values such as English Dictionary, Thesaurus, Wikidata to learn and find relationships and new values of same type.

Results are explained with reasons which unique in the Industry. It is easy to understand why a particular value is grouped or relationship is made. Algorithm is Data and Volume agnostic. Can be used any type, volume of data. It can also work in any language including Chinese and Japanese.

Algorithms created using Graph based Sequence to Sequence Prediction methods. Entire Technology built from scratch. NaturalText uses Python and Rust for coding.

2. Text Analytics

Converting unstructured data into structured data : Using Intent Recognition and Extraction in combination with Named Entity Recognition, text data can be converted to structured data that can be fed to the databases etc.

Clustering in Detail : Sentence Level or Fine Grained clustering can be applied any language text data. Documents can be clustered using sentence level clustering ranking.

Using Natural Language Generation for creating text reports from tabular column of data. This can be used to automate report generation or to enhance the capabilities of content writers.

Multiple Languages supported. Translation will be offered in future.

3. Examples

All the examples can be applied to any language or combination of text and numbers.

Example 1 : Intent Recognition and Extraction

Company A made profit of $200 in year ending 2016


Name : Company A ; Action : Profit ; Amount : $200 ; Date : 2016

Example 2 Sentence Level Clustering

these three sentences can be clustered as

Company A havent made any profit this year , Company Z have made some loss this year

Enterprise M performing good.

4 Assumptions Made for Financial Sector data

Assumption 1 All the documents contain some structure which can be discovered by an Machine Learning Algorithm.

Documents written in either Natural Language such as English or Structured data such as in HTML/Database format. In other words, documents are not random distribution of numbers/alphabets.

Assumption 2 Same class of Documents share same kind of structure with small variations.

example, Business reports describe company performance using English sentences along with tables of data. Emails mostly contain text data.

5 Training Algorithm for specific tasks

As this is trainable algorithm, using Rosetta Stone like comparable data to train the algorithm is possible. If a data exists in two formats ie one in text, another in Spreadsheet , algorithm can be trained to make connections using that data, which can be applied to new documents.

6 Cloud Deployment and Data Protection

NaturalText Application can only be deployed Cloud based datacenters as it needs to be trained and models needs to be created before it can be applied to real data.

Standard Data Protection and Compliance is offered including Third Party Auditing. For example, AWS deployment has its own Data Protection and Security Services which can used when hosting in AWS.

7 About NaturalText

Started 2015, NaturalText team is working in Language and Machine Learning Technologies to solve the issues in Natural Language Understanding, Artificial Intelligence and Reasoning. It is part of Siva Raja Technologies Pvt Ltd, and funded by family and friends. NaturalText is based on Chennai, India.

NaturalText accepted into Nasscom DeepTech club on Nov 2017.

Rajasankar Viswanathan is the Founder of NaturalText, has 15 years of experience in Software Industry.

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