Parascript Innovates Natural Language Processing for Unstructured Document Automation

Natural Language Processing (NLP) has various use cases within cutting-edge technology, and Parascript, a software company based in Colorado, has developed innovative NLP technology that finds data by analyzing context within a document. Intelligent capture has commonly been used to automate structured and semi-structured documents, but NLP can assist with automatically locating and extracting data from complex unstructured documents, even if the desired information is phrased in diverse ways. This is attainable using artificial intelligence and machine learning trained to identify phrases using context, no matter where they are located in the document.

Parascript uses NLP within the boundaries of Intelligent Document Processing (IDP) as part of the data location and extraction process, turning unstructured data into structured data (standardized output) for use in other systems. Applying this technique to modern IDP solutions opens the door to full automation of complex document processes.

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What is NLP?

Natural Language Processing is the set of procedures used to break down text into segments that software will be able to understand. NLP-based document processing uses linguistic features and usually involves three steps:

• Understand sentence segmentation and sentence composition—in this step, each sentence is broken down into words
• The words are tagged and labeled grammatically by their role in the sentence, for example, nouns, verbs, adjectives
• Phrase chunking analyzes segments of the sentence and compares to surrounding sentences to determine how those sentences relate to each other

These parts comprise the deconstruction of text which then is fed into artificial intelligence algorithms. The resulting output contains phrases that were automatically identified by the AI in various formats.

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Parascript NLP Differentials

To ensure high-accuracy extraction from unstructured documents, traditional NLP technology requires users to identify the specific details needed for a particular task. For example, the key verbs, nouns, and adjectives are entered manually, and dictionaries and linguistic structures are encoded. The NLP software then analyzes the data and organizes it as needed. This process requires time-consuming preparation and significant amounts of sample data.

Parascript NLP technology eliminates the need for this time and effort using an alternative machine learning approach. This approach reduces the preparation time required from a human operator by allowing the NLP system to automatically analyze and train on a very limited sample data set (3-50 samples). Parascript has made great strides in machine learning, and their proprietary technology can dramatically reduce deployment time to enable higher accuracy when extracting data from unstructured documents. Below are technical features that make Parascript’s NLP technology unique:

• Applies Extreme Learning Machine (ELM) algorithms
• Involves lexical semantics analysis
• Implements word embedding techniques to capture semantic properties of words
• Utilizes many more proprietary methods and algorithms

How is NLP Used?

Overall, Parascript’s Natural Language Processing is a significant advancement in the automation of complex unstructured documents. It can be used to locate and extract paragraphs of text in documents with similar meaning to paragraphs used in training, and it can process non-standardized documents that were previously difficult or impossible to automate. Examples include, locating paragraphs related to the legal description of a property within contracts or detecting restrictive language in Deeds of Trust. When it comes to locating target data within text paragraphs, Parascript’s NLP technology can extract key contractual terms in legal documents or entities in unstructured documents. Additionally, this software can provide sentiment analysis of a document (e.g., positive, negative, or neutral). Parascript’s NLP technology pushes the industry of document automation forward with new use cases continuing to develop.

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