How To Use NLP for Comparative Semantics and Analytics

How to use NLP for Comparative Semantics and Analytics

The simplest way to track your compliance is to compare what you are doing to what you are supposed to do. Doing this on text-heavy documents like contracts, leases, emails, VDR data, and so on, and referencing that against dense regulations and publications, is either time-consuming or expensive to outsource. Learn how Natural Language Processing with Comparative Search and Analytics can help.

Example:

A client needed to identify critical contracts and potential violations more efficiently to comply with new financial regulations, most notably the Dodd-Frank Act. Previously, the client sent their vendor contract to an off-shore 3rd party to analyze. The process was manual and expensive and still required by the client. Further no knowledge was extracted during the process, so it continued without improvements.

Lymba’s Solution: was to implement a semantic method to process and compare contracts against bank’s definitions and interpretations of the Dodd-Frank Act to automatically pick up potential issues without initial human reading.

We did this by training the system

• Labelling of provision-relevant knowledge in sample snippets

• Identification of provision paragraphs for training contracts

• Creation of machine learning classification models

Then the contracts were pushed through the trained pipeline. The client was then provided a summarization of documents and any potential violations. And the document could be searched for sentences, paragraphs or whole sections. Ultimately, comparative search lets you find the most similar instance of text related to another snippet of information.

With a customized K-Extractor, the system can identify new named entity types by relabeling existing types, or labeling new entity types. The customized K-Extractor can also identify relevant semantic relations with Semantic Calculus.

This includes:

Capturing the semantics of contract provisions

Associating temporal constraints/penalties to contract clauses/attributes

Bind clauses to one or both parties

…. And Identifying exclusions

Finally, Machine learning classification models are able to label entire paragraphs for ease of categorization and observation.

Hopefully you now have a better understanding of how use NLP for comparative semantics and analytics can be utilized in your use case. Thanks for watching from LYMBA. Please reach out to us with any questions or for help on your next project.