Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) that uses computational tools to tag, translate, summarize, analyze, and extract data for review. The result is a technology platform capable of "understanding" the contents of documents, including the contextual nuances of the language within them.  For example, AI-based legal software can flag an exclusivity provision as an urgent issue requiring review by a senior attorney.

Recent advances have enabled AI in legal tech to deliver NLP with greater speed and accuracy. The AI not only processes and extrapolates language in the way a human might, but it can also finetune its performance with each update from a human reviewer to a company’s AI Digital Playbook.

When a corporate legal department or procurement team negotiates a large volume of agreements, it’s all too easy to overlook omissions or non-standard clauses. Natural Language Processing adds another set of “eyes” to quickly and efficiently scan contracts for errors, omissions, and deviations. NLP works in real-time, 24/7, applying uniform criteria to all agreements to ensure consistency and accuracy in contract review and negotiation.

In this article, we’ll explore past NLP challenges, answer the basic question: “How does clause extraction NLP work?”, and reveal outcomes legal companies or departments can achieve by leveraging NLP technology in their contractual review processes. 

Overcoming Natural Language Processing Challenges

In the past, machines have struggled to process how humans write. Software engineers have been working on these issues for over a decade to develop advanced AI that not only understands us but analyzes and learns like us. Good NLP takes into consideration these factors to overcome past NLP challenges:

• Preliminary analytical breakdown categorizes the text. NLP relies on preprocessing, standardization, and conversions that break down language into meaningful tokens. 
  
  
• A instructional-based approach defines the system. Engineers use grammatical guidebooks, company negotiation playbooks, templates, best practice examples, and organizational governance to inform the AI.
  
  
• Human training refines the system. Training data and human-centered comments feed statistical methods to improve performance and ensure compliance with established standards. 

How Does Clause Extraction NLP Work?

Enterprises employ NLP to extract relevant information from contracts with AI-powered technology that breaks human language into fragments to aid in understanding sentence structure, grammar, and context. This process allows computers to scan written text as a human might. 

Full-text extraction involves the following processes:

• Extracting entities such as companies, people, dollar amounts, dates, key clause types
  
  
• Categorizing clauses for function, purpose, contract type, positive or negative position
  
  
• Comparing content to past agreements, company playbooks, templates, best practices 
  
  
• Extracting data for automated alerts, trend reports, visualization, or priority review

The exact mechanisms of NLP functionality rely on advanced technical programming. Fortunately, though, the resulting user experience is a natural one akin to communication with another member of their team, with virtually no training required. Once teams have access to the platform, the document review process can be as simple as sending an email. 

Backend processes execute the following steps:

1. Preprocessing work converts lexical words, phrases, and syntactic markers into usable data.

Processes include:

• Structure extraction to identify fields of content worth marking for analysis. 
• Speech tagging identifies nouns, verbs, adjectives, adverbs, and pronouns. 
• Phrase extraction may emphasize “big data” vs. “big” and “data” used separately.
• Entity extraction tags relevant people, places, companies, units, dollars, etc.

2. Gain a holistic understanding of the agreement through Macro NLP processing.

Macro understanding generates information regarding the document as a whole. Statistical techniques like clustering, categorization, and summarization provide insight into contextual appropriateness, approval likelihood, and playbook adherence. Risk is quickly analyzed, rated, and flagged for the entire document in mere minutes.

Macro techniques are helpful for:

• Classifying and organizing complex contracts.
• Clustering records and generating tailored reports.
• Extracting key clauses.
• Finding errors and omissions.

3. Examine details with Micro NLP processing.

Micro understanding can be helpful when searching for a specific provision that needs updating. 

Micro understanding can help with:

• Defining acronyms to clear up passages.
• Extracting citation references to other documents.
• Noting key people, companies, products, locations, dates, and dollar amounts.
• Reviewing contract terms, regulatory requirements, and upcoming renewals.

How LexCheck’s NLP Can Help You Streamline Legal Contracts

LexCheck is an AI and NLP-powered contract review and negotiation solution that automates the review, revision, and negotiation process for legal and procurement teams. Contracts can be relatively simple, such as Non-Disclosure Agreements, or span hundreds of pages, as in a Merger & Acquisition agreement. 

Automated contract review uses NLP for clause extraction to ensure contracts adhere to a company’s playbook standards. LexCheck analyzes information against the company’s Digital Playbook to ensure preferred language and detect anomalies and outliers, returning the document with context-based markup in a matter of minutes.