In legal tech, artificial intelligence (AI) has revolutionized the way legal professionals analyze and process vast amounts of legal information. New applications for legal AI tools are being developed and introduced rapidly as the technology expands.

One significant application of AI in the legal domain is entity detection, which enables the extraction of relevant information, such as Defined Terms, from legal documents.

In addition to understanding how AI facilitates this process at scale, it’s important to recognize why entity detection matters and the use cases for extracting Defined Terms in legal documents.

Understanding Entities and AI Extraction

In the context of natural language processing (NLP), an entity refers to any object or concept within a text that can be defined and distinguished. Typical examples include names of people, organizations, locations, time expressions, quantities, and monetary values.

Entity extraction, also known as named entity recognition (NER), is an AI-based process that identifies and classifies these named entities into predefined categories. Using AI for entity extraction allows users to pinpoint the key elements in a large corpus of text automatically, saving valuable time and resources.

AI-powered entity detection models utilize NLP techniques to identify and extract these entities, providing valuable insights for legal analysis and document understanding. The extraction of these entities is crucial for understanding and interpreting textual information efficiently.

Defined Terms in Legal Documents

Defined Terms are recurring terms within legal documents that are explicitly defined to carry specific meanings throughout the text. These terms are typically capitalized and provide consistency and clarity in the interpretation of the document. Examples of Defined Terms include "Contract," "Buyer," "Licensor," and "Indemnification.” Detecting and extracting Defined Terms from legal documents is crucial for understanding the relationships and obligations described within the text.

Use Cases for Detecting Defined Terms

The detection of Defined Terms in legal documents serves various important use cases. Analyzing and reviewing documents, as well as drafting and managing contracts are all areas in which detecting and extracting Defined Terms might be useful for legal professionals.

Document Analysis and Review

By automatically extracting Defined Terms, legal professionals can efficiently navigate through complex legal contracts and understand the interconnections and implications of specific terms. Instead of using built-in Word features like “Find” and “Replace” for each key term, AI can extract defined terms more broadly—helping to avoid costly errors such as overlooking a Defined Term they may have forgotten to search for.

Detecting Defined Terms is vital to analysis and review for a few key reasons. For instance, it is important to resolve when a Defined Term is used but not defined, or the inverse, when a Defined Term is defined but not used. Additionally, it’s important to locate when Defined Terms are given multiple or conflicting definitions.

Entity detection and extraction can help surface these issues, and furthermore, some tools—like LexCheck—can leverage generative AI to suggest corrections for missing definitions or even support a definition repository based on an organization’s specific guidelines.

The rapid extraction of Defined Terms by an AI tool can also assist legal professionals in analyzing large collections of legal documents, enabling them to quickly identify relevant provisions and precedents. This process is scalable, too, and can be used effectively to aid in research involving data that would be uneconomical for a human researcher to complete alone. For example, utilizing this technology can enable users to search for "Confidential Information" across the 100 most recent NDAs that the company executed.

Contract Drafting and Management

Detecting these terms can aid in drafting legal documents. A database of Defined Terms from related documents can be used as a reference to ensure consistent language and meanings, minimizing potential confusion or misinterpretation.

Additionally, 69% of contracts don’t follow a contract playbook, and 71% of contracts are not monitored for deviations from standard terms. By identifying Defined Terms within contracts, organizations are enabled to maintain consistency and avoid ambiguity in contractual agreements, ensuring compliance and reducing the risk of misunderstandings or disputes.

This safety net can benefit the large majority of organizations operating without a standard contract playbook as well as organizations with a pre-established guiding document.

Training an Entity Detection Model for Defined Terms

Training an AI model to detect and extract Defined Terms involves a combination of supervised learning and domain expertise.

Given the diversity of legal language and the specific, context-dependent nature of Defined Terms, this process may be more complex than training a model to identify more common and universally defined entities. Thus, it's crucial to use a diverse and comprehensive training dataset that reflects the range of language, structure, and terminology used in legal documents.

The process generally consists of the following steps:

Dataset Preparation: Curating a labeled dataset of legal documents, where Defined Terms are annotated to train the model. Domain experts play a crucial role in identifying and labeling the Defined Terms accurately.

Feature Extraction: Transforming the legal text into numerical features that capture relevant patterns and contextual information. Techniques such as word embeddings, part-of-speech tagging, and Keyword extraction are commonly employed.

Model Training: Employing self-supervised machine learning algorithms, such as deep learning models (e.g. transformers), to learn from the large dataset, followed by fine tuning on labeled data and optimizing the entity detection process.

Iterative Refinement: Fine-tuning the model through iterative feedback loops, involving constant evaluation, error analysis, and retraining to enhance accuracy and performance.

AI at Scale and Quality Assurance

AI technologies have revolutionized the ability to detect Defined Terms at scale and with high quality. These tools are highly efficient, processing vast amounts of text quickly, which reduces the time and effort required for manual review.

AI’s ability to consistently identify and extract Defined Terms across multiple documents helps to minimize errors and discrepancies, and the process is continually improved through feedback loops—enabling the model to learn from mistakes and enhance accuracy over time. Additionally, entity detection models can be trained on multiple legal document types and adapt to different contexts, accommodating the diversity of legal terminology and conventions.

Transforming Legal with AI

Entity detection powered by AI has transformed the way legal professionals extract Defined Terms from legal documents. Leveraging supervised entity detection for higher accuracy and coverage paired with utilizing generative AI for recommended edits will bring the best of both together. By automating the process of entity detection, legal tech solutions streamline document analysis, contract management, and legal research, leading to increased efficiency and accuracy. 

While challenges lie ahead, particularly in training models to understand the complex, context-dependent nature of defined terms, the progress already made is promising. As AI technologies continue to advance, we can expect even greater improvements in the extraction and understanding of legal information, ultimately revolutionizing the legal industry as a whole.