Solutions

Identify Named Entities in a document

Date: 2021-10-12

Named Entity Recognition (NER) identifies the key semantic elements of a sentence. A named entity must designate a perceptible reality and represent an element essential to the understanding of a text such as, for example: a name, an address, an email, a phone number, a city, a date, etc.

[Client issue] How to deal with a wide range of documents?

Documentation is one of the main assets accumulated by companies and administrations. The stored data is a treasure trove of information on which these entities must capitalize: names of customers, partners, contracts, etc.

However, the manual analysis, processing and archiving of documents are long and tedious tasks. The diversity of documents that have their own specificities (contracts, customer files, product files, etc.) makes their management more complex, and they are often neglected because of their human and financial cost.

Many legal teams want to have quick access to specific information in a contract, such as the payment date, in order to avoid missing a deadline, for example.

[Pain Points] Time consuming tasks - Document diversity - Data classification

[Our Solution] Automate the extraction and classification of named entities from a document

Lettria's technology offers a dual approach that combines machine learning with regular expressions.

Our solution, applied to a company's legal department for example, enables the rapid and automatic isolation of key information from a contract (dates, stakeholders, etc.) via a named entity detection tool, making it easier to process priority deadlines.

Essential information is automatically extracted and classified, making it easier to find and archive. The time savings are enormous and allow the process to be optimized by reducing processing costs.

Lettria's offer allows you to eliminate non-productive tasks with little added value, and also to gain efficiency by facilitating access to information.

[Solution advantages] - Back office automation - Process optimization and cost reduction - Improved document processing efficiency.

[Uses cases]

  • Retail: Our solution allows you to tag and classify a wide range of documents such as product sheets and extract all the essential key information such as price, name, quantity available, delivery times, etc.
  • Insurance: The analysis of contracts and offers is facilitated by the detection of named entities. Contracts can be automatically analyzed to extract the names of the parties involved, the validity dates, the main clauses, etc.
  • HR: The identification of key information in a document, applied to a CV for example, facilitates screening tasks thanks to the extraction of essential data.

Author : Hugo Poli

5 minutes read

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