Clause extraction and clause classification are two essential techniques used in contract analysis to automate and simplify legal document reviews. Here’s the difference:
- Clause Extraction: Identifies and locates specific clauses in contracts (e.g., payment terms, termination clauses). It answers the question, “Where is this clause?” by pinpointing exact locations.
- Clause Classification: Categorizes these clauses into predefined groups (e.g., termination, indemnification). It answers, “What type of clause is this?” by interpreting the clause’s purpose or function.
Both methods convert unstructured legal text into structured data, reducing manual effort and improving accuracy. Extraction is ideal for locating specific terms, while classification helps organize and assess risks in contracts. Together, they streamline contract management, enabling faster reviews and better compliance.
Quick Comparison
| Aspect | Clause Extraction | Clause Classification |
|---|---|---|
| Purpose | Locate specific clauses in contracts | Categorize clauses by type or function |
| Key Question | "Where is this clause?" | "What type of clause is this?" |
| Methodology | Token sequence labeling | Text classification on identified segments |
| Use Case | Pinpointing clause locations | Organizing and analyzing clause categories |
Understanding when to use each technique – or combine them – can help legal teams save time, reduce errors, and make better decisions.
What Is Clause Extraction?
Clause extraction is a game-changer for automating contract reviews. It takes unstructured legal text and turns it into structured data, making analysis much faster and more precise. This AI-powered process identifies and locates specific clauses or provisions within contracts, allowing computers to scan the text, understand its context, and extract key phrases at lightning speed. By breaking down complex contract language into smaller fragments, it pulls out important entities and categorizes clauses with pinpoint accuracy.
This technique can identify critical contract elements like payment terms, termination dates, indemnification obligations, exclusivity clauses, default provisions, notice requirements, and change of control clauses. For example, it can extract exclusivity, payment, or termination terms across different types of contracts. What makes this process particularly effective is its ability to recognize clauses written in various ways. Take these two examples: "Licensor hereby grants Licensee a fully paid, worldwide, exclusive license" and "Buyer will purchase 100% of its requirements for [good] from Seller." Both describe exclusivity, even though the second one doesn’t explicitly use the word "exclusive".
Understanding how clause extraction works is essential for seeing how it simplifies contract management.
How Clause Extraction Works
The process kicks off with preprocessing, where the text is reformatted to make it machine-readable. Next, the AI model analyzes segments of text – ranging from just a few words to entire paragraphs – to determine whether a particular section qualifies as a specific clause type. It uses techniques like Named Entity Recognition (NER), relation extraction, and text classification to convert unstructured legal language into structured data. For instance, it can extract metadata such as effective dates, the identities of the parties involved, aliases, and even detailed relational information like NOTICE_PARTY, NAME, TITLE, ADDRESS, and EMAIL.
Modern approaches to clause extraction leverage advanced neural networks and IOB (Inside-Outside-Begin) tagging to mark clause boundaries with exceptional precision, often outperforming older methods on English-language datasets. Once the information is extracted, it’s organized using a structured labeling system that includes three key components: Span Highlight (pinpointing where the clause appears), Tag (identifying the clause type), and Attribute (adding extra details about the clause). A "divide and conquer" strategy is used to handle complex sentences, first isolating shorter components with NER and then applying targeted models for further refinement.
Benefits of Clause Extraction
The advantages of clause extraction are clear and impactful, especially for contract management. One major benefit is speed – automating this process cuts review times from hours to just minutes, delivering consistent results and enabling quicker risk assessments.
Another benefit is accuracy. By reliably identifying key terms across all contracts, clause extraction reduces the risk of human error and ensures no obligations or non-compliant provisions are overlooked. Plus, it can process hundreds – or even thousands – of contracts at once, consistently identifying the same types of clauses with uniform precision.
Finally, accurate clause extraction is the backbone of effective contract analysis. When clauses are identified correctly, subsequent tasks like entity extraction, answer generation, and normalization become much more reliable. On the flip side, if clause detection isn’t accurate, errors can snowball in later stages. By turning tedious legal reviews into a strategic, data-driven process, clause extraction helps legal teams quickly locate critical clauses across their entire contract portfolio, boosting compliance and improving oversight of contractual obligations.
What Is Clause Classification?
Clause classification is a process that uses automation to sort contract clauses into specific categories like termination, indemnification, confidentiality, exclusivity, and non-competition provisions. By training machine learning models on annotated legal texts, clauses are assigned to these predefined groups. This method transforms unstructured contract language into structured, usable data, making it easier to analyze and manage entire portfolios of contracts. Essentially, it converts raw legal text into organized insights that can be acted upon.
Unlike token-level analysis, clause classification focuses on entire clauses. Contracts often express similar provisions in different ways – for instance, exclusivity, non-competition, and most-favored-treatment clauses can be written in a variety of formats. To handle this variability, the models use contextual embeddings and advanced neural architectures to grasp the deeper meaning of clauses, ensuring they can adapt to the diverse language found in legal documents.
How Clause Classification Works
Clause classification can follow two approaches: analyzing clauses independently (ICC) or considering their context within the document (JCC). In the ICC approach, each clause is evaluated separately using techniques like cross-entropy loss during training. On the other hand, the JCC method uses Long Short-Term Memory (LSTM) modules to encode individual clauses and then employs a bidirectional LSTM-based encoder to update these representations by taking inter-clause relationships into account.
To begin, clause boundaries are identified through manual annotation or syntactic parsing. Neural models, paired with contextual embeddings such as those generated by BERT, then classify the text segments into categories. Evaluations often show that clause-level classification achieves higher F1 scores compared to token-level methods, though outcomes can vary depending on the dataset. These capabilities make clause classification a powerful tool for contract review.
Benefits of Clause Classification
Clause classification goes beyond merely extracting key contract details – it organizes them into meaningful categories, enabling more thorough analysis. One of its standout advantages is speed. Tasks like risk assessments, which might take hours manually, can now be completed in minutes. By reducing the time spent on manual reviews, the process also minimizes errors and allows legal teams to focus on more complex analyses.
Another major benefit is its ability to enhance risk analysis through precise categorization. Automated classification can flag unusual or non-compliant clauses that could pose legal or financial risks. By consistently categorizing clauses across an organization’s contracts, it becomes easier to identify agreements with specific terms and compare standard provisions across documents.
Additionally, clause classification lays the groundwork for advanced contract analysis. Structured data generated through accurate classification supports further tasks like entity extraction, question answering, and normalization. When classification accuracy reaches levels of 75% or higher, these downstream processes become even more reliable, offering deeper insights into contract terms.
The method also enables automated workflows that would be nearly impossible with manual review. By organizing contracts, grouping clauses by type, and generating detailed reports, clause classification turns static documents into actionable intelligence. For businesses managing large contract portfolios, this structured approach is invaluable for compliance checks, comparative analysis, and informed decision-making.
Main Differences Between Clause Extraction and Classification
Clause extraction and classification both play a role in transforming unstructured legal text into structured data, but they serve distinct purposes in contract analysis. Clause extraction focuses on pinpointing the exact location of a clause within a contract – essentially answering the question, "Where is this clause?" On the other hand, clause classification categorizes these extracted clauses, assigning labels like termination, indemnification, or exclusivity to answer, "What type of clause is this?". While their goals overlap in some ways, the technical methods used by each approach set them apart.
The methodologies behind these processes are quite different. Extraction techniques, such as Named Entity Recognition, identify continuous spans of text, even when clauses are written in non-standard language or appear in low-quality scans. These methods work directly with token sequences to mark the start and end points of clauses with precision. Classification, however, takes the identified segments and applies text categorization techniques to assign them to one or more categories. This step often relies on pre-annotated data or syntactic parsing, which can introduce additional errors. For example, a study comparing these approaches on four English datasets found that token sequence labeling (used in extraction) outperformed classification in three of the datasets.
Accuracy in clause extraction is critical because it forms the foundation for all subsequent tasks. For instance, if an extraction model identifies a clause with only 75% accuracy, any downstream processes – like classification or entity extraction – are likely to inherit and amplify those errors. Both techniques also face the challenge of handling the variability in how clauses are phrased. Take exclusivity clauses, for example: one might explicitly state, "Licensor hereby grants Licensee a fully paid, worldwide, exclusive license", while another might imply exclusivity more subtly, such as, "Buyer will purchase 100% of its requirements for [good] from Seller." Extraction models need to detect clause boundaries despite these differences, while classification models must interpret the intent behind non-standard phrasing.
Annotation requirements further differentiate the two methods. For extraction, annotations typically involve "span highlights" that define the boundaries of a clause. This makes the task more focused on identifying where clauses begin and end. Classification, by contrast, requires assigning detailed tags and sometimes additional attributes to represent the clause’s meaning, making it a more complex and expertise-driven process. The table below summarizes the key differences between clause extraction and classification.
Comparison Table: Extraction vs. Classification
| Aspect | Clause Extraction | Clause Classification |
|---|---|---|
| Primary Purpose | Locates specific clauses within contracts | Categorizes clauses by type or function |
| Key Question Answered | "Where is this clause?" | "What type of clause is this?" |
| Methodology | Token sequence labeling with span highlighting | Text classification on pre-identified segments |
| Precision Level | Provides exact start and end points | May miss exact boundaries |
| Technical Dependencies | Directly processes token sequences | Requires clause annotations or syntactic parsing |
| Error Risk | Lower, due to direct extraction | Higher, due to reliance on additional processing steps |
| Performance on English Data | Superior performance on most datasets (3 out of 4 tested) | Generally higher absolute F1 scores but lower token-level accuracy |
| Annotation Requirements | Requires span highlights to define clause boundaries | Requires detailed tags and attributes for categorization |
| Annotation Complexity | Less demanding – focuses on detecting boundaries | More demanding – requires interpretation of clause meaning |
| Downstream Impact | Provides a solid foundation for further analysis | Errors may compound in dependent tasks |
| Ideal Use Case | Pinpointing exact clause locations and boundaries | Organizing and categorizing identified clauses |
| Risk Management Benefit | Helps locate risk-relevant provisions | Enables assignment of risk ratings and flagging of critical clauses |
Understanding these differences can help guide the choice between extraction and classification when automating contract analysis. If the goal is to quickly locate specific provisions for manual review or to create a searchable database, extraction is often sufficient. However, classification becomes essential when tasks like automated routing, risk assessment, or compliance monitoring are involved. In practice, combining both techniques can offer the best results: extraction ensures accurate clause identification, while classification organizes the data for further automated decision-making.
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How These Techniques Apply to Contract Management
Clause extraction and classification bring practical advantages to contract management by turning unstructured legal documents into well-organized, actionable data. These Natural Language Processing (NLP) methods help teams stay compliant, reduce risks, and make smarter decisions.
Manual reviews can miss critical provisions, make tracking payment obligations difficult, and complicate compliance efforts. As explained earlier, extraction and classification tackle these challenges in specific ways, depending on the organization’s needs.
When to Use Clause Extraction
Clause extraction is ideal for identifying and isolating specific contractual elements that demand immediate attention. This technique scans contracts to locate and extract exact provisions, answering the question, "Where is this information?" rather than "What kind of information is this?"
For example, extraction can automate the identification of renewal dates, payment terms, and other obligations, cutting review time by as much as 87.5%. Imagine a company managing hundreds of vendor agreements – it can extract payment terms from all contracts to check for consistency, spot outliers, and address discrepancies before they become major issues.
In a recent audit, Kieran Lynch, Head of Legal and Compliance at Rakuten Europe, used Trackado to export a list of new contract partners for the year. This highlights how extraction tools simplify audits and improve compliance reporting.
This automation reduces review time from hours to minutes and minimizes the risk of human error. By handling routine document reviews more efficiently, legal teams can shift their focus to higher-level strategic tasks.
While extraction pinpoints key details, classification takes it a step further by organizing those details into actionable categories.
When to Use Clause Classification
Clause classification is essential when contracts need to be organized, categorized, and analyzed at scale. Instead of just locating clauses, classification groups them into specific categories – like indemnification, termination, exclusivity, or force majeure – enabling systematic risk assessments and informed decision-making.
By categorizing clauses across an entire portfolio, organizations can detect patterns, evaluate risks, and flag problematic provisions. For instance, a financial services firm might classify all change-of-control provisions to ensure that merger or acquisition activities don’t trigger unfavorable obligations. This process can transform legal departments into strategic hubs by automatically identifying non-compliant or unusual clauses.
Peter Skeen, Head of Legal, Procurement, and Company Secretarial at Good Energy, leveraged Trackado to analyze their contract library. This allowed them to identify common contract types and improve oversight of key dates, moving beyond outdated spreadsheet-based methods.
Classification also streamlines workflows, such as routing leases to real estate specialists or licenses to intellectual property lawyers based on the clauses they contain. Beyond organization, it ensures adherence to playbooks by comparing contract provisions to approved templates, flagging non-standard terms. Compliance monitoring becomes more proactive, with systems scanning for clauses that require special attention, such as those related to data privacy or regulatory compliance. Categorized provisions also enable benchmarking, helping organizations identify trends, compare terms to industry standards, and make data-driven decisions during negotiations.
The Power of Combining Both Techniques
Using both extraction and classification together often yields the best results. Extraction lays the groundwork by accurately identifying clause locations, while classification organizes that information for deeper analysis and action. The relationship between the two is critical – if extraction is only 75% accurate, classification will perform even worse due to compounding errors. When combined, these techniques empower organizations to manage contracts proactively, blending precise data capture with insightful categorization for smarter, more efficient workflows.
Technical Requirements for Implementation
Setting up effective systems for clause extraction and classification requires solid infrastructure, high-quality training data, and specialized expertise. Before diving into deployment, organizations need a clear understanding of the technical demands involved.
Preprocessing is the first step. It transforms unstructured documents into data that can be analyzed. This involves tasks like document assembly, sentence detection, and tokenization. Once that’s done, Natural Language Processing (NLP) models can step in, using techniques such as Named Entity Recognition (NER), relation extraction, and text classification. These tools help convert the often messy language of contracts into structured, usable information.
The backend infrastructure operates in layers. First, a classifier identifies whether a document is a contract or not. Then, more detailed classification assigns specific types of contracts to the right reviewers. This layered approach ensures that the right models are applied to the right documents, bridging the gap between initial preprocessing and advanced model deployment.
Training data quality is critical. The amount of data required depends on the complexity of the task. For example, provision extraction typically needs less data than answer extraction, as the latter involves both identifying and interpreting a clause. Many organizations rely on hundreds of clause classification models to automatically annotate agreement clauses across various categories. Each provision in the training set must be accurately labeled, as poor-quality annotations can severely impact model performance.
Annotation involves three key components:
- Span Highlight: Highlighting key phrases or sentences containing the provision.
- Tag: Assigning a classification label that defines the provision type.
- Attribute: Adding structural details to clarify the clause.
This is not a task for general annotators. It requires legal professionals or specialized teams who understand contract structures and can navigate ambiguities.
Errors in detection can snowball into downstream tasks, making it essential to get the foundational steps right from the start.
Using Pre-Trained Language Models
Once preprocessing and annotation are in place, pre-trained language models can significantly enhance extraction and classification accuracy. Models like BERT represent a leap forward compared to traditional NLP techniques. While older methods focus on breaking language into components like syntax and grammar, pre-trained models excel at understanding context and nuance in legal texts.
Their major advantage lies in their ability to recognize patterns from vast datasets, enabling them to interpret legal documents with precision. For instance, these models can identify clauses with similar meanings, even if they’re worded differently. A clause like "Buyer will purchase 100% of its requirements for [good] from Seller" implies exclusivity, even without explicitly using the term "exclusive".
Pre-trained models also handle challenges like poor-quality scans and unfamiliar text variations, which are common in legal documents. They employ advanced statistical techniques, including clustering and summarization, to provide a comprehensive understanding of agreements. This allows for faster risk assessment and flagging.
In comparative studies across four English datasets, token sequence labeling using bidirectional LSTM architectures with attention layers achieved better results on three out of four datasets. These architectures capture the context that simpler models often miss, making them especially effective for processing legal language.
To improve accuracy in clause-specific tasks, organizations should use sentence embeddings at the paragraph level rather than the document level. Similarly, relation extraction should focus on specific clauses rather than entire documents. For instance, introductory-clause relation extraction should target only the introductory clauses.
Challenges in Processing Legal Language
Legal language introduces unique challenges that complicate extraction and classification tasks. One major hurdle is the specialized terminology and phrasing conventions that differ greatly from everyday language. For example, an exclusivity clause might say, "Licensor hereby grants Licensee a fully paid, worldwide, exclusive license", or it could be phrased as "Buyer will purchase 100% of its requirements for [good] from Seller." Models need to recognize that these are functionally identical despite their different wording.
Key contract clauses, such as those dealing with exclusivity, non-competition, or pricing, are often expressed in highly variable ways, making it difficult for models trained on standard examples to perform well. Poor-quality scans add another layer of complexity, obscuring standard clause patterns.
Legal documents are notorious for long, complex sentences, often separated by commas or semicolons without clear breaks. This structure makes it hard to pinpoint where relevant information begins and ends. Parsing such sentences requires advanced capabilities that go beyond general-purpose NLP models.
Even syntactic parsing, used to identify clause boundaries, carries risks of error. Studies show that right boundary matching in clause extraction consistently outperforms left boundary matching across most datasets tested, except for the EmotionStimulus dataset. This highlights how challenging it can be to determine where clauses start and stop, affecting overall system accuracy.
To address these issues, organizations must invest in expert annotation teams. These teams should be equipped to handle the nuances of legal language, including ambiguous phrasing and domain-specific terms. High-quality labeling is vital for managing risks, as it enables systems to automatically detect unusual or non-compliant clauses that could lead to legal or financial problems.
Finally, organizations should prioritize clause detection accuracy before moving on to downstream tasks like relation extraction. Errors in early stages can cascade, amplifying inaccuracies as the process continues. Starting with reliable provision extraction provides the strongest foundation for managing the complexity of legal language.
How to Choose the Right Approach
Deciding on the best method for contract management depends on the specific needs of your contracts. Matching the right technique to your portfolio’s nature ensures effective results.
The characteristics of your contract portfolio are key. For standardized, template-based contracts where clauses are consistently positioned, extraction alone often works well. These contracts have predictable structures, making it easier to locate provisions. On the other hand, contracts with varied phrasing require classification to interpret the diverse language used.
If you’re dealing with poor-quality scans or documents with OCR errors, combining extraction and classification becomes necessary. Legacy documents and third-party contracts, which often deviate from internal standards, also call for classification to assess risks accurately.
The complexity and volume of your contracts further shape your approach. High-volume, low-complexity contracts benefit from extraction’s speed and efficiency. However, more complex agreements – like M&A deals, licensing contracts, or regulatory compliance documents – require both precise data capture and robust risk assessment. Tailoring your strategy to these factors ensures optimal contract management.
Using Extraction Alone
Extraction is a fast and efficient way to pinpoint key data in contracts. This method excels at identifying critical information like key dates, payment terms, and contact details, significantly cutting down review times.
For instance, tracking renewal dates is a straightforward use case. Similarly, locating payment terms helps finance teams with budgeting and forecasting, while identifying termination clauses supports contract lifecycle management. In these cases, the goal is to find specific information, not analyze its meaning.
This approach works especially well for organizations with straightforward contract review needs, limited legal resources, or a high volume of contracts requiring quick processing. The main advantage here is speed – extraction focuses on finding where specific data resides in the document without the complexity of classification systems.
Due diligence processes also benefit from extraction alone. When working with standardized templates and minimal clause variations, extraction provides a quick route to the necessary information.
Using Classification Alone
While extraction focuses on locating data, classification interprets and categorizes risks in the contract language. This approach is ideal for assessing risk levels, ensuring compliance, or organizing contracts by strategic importance. Classification transforms contract review into a risk management process.
A great example is identifying non-compliant or unusual boilerplate clauses. Classification can detect deviations from company standards, even when the language varies significantly. This ability is crucial for maintaining consistency and minimizing hidden risks.
Classification also allows for organizing contracts by risk level – high, medium, or low – so legal teams can prioritize their efforts. High-risk agreements get immediate attention, while lower-risk contracts move through workflows more efficiently.
Industries like healthcare, finance, or real estate, which face strict compliance demands, rely heavily on classification. Clauses such as indemnification, force majeure, and exclusivity provisions often carry significant legal or financial weight. Classification helps by systematically flagging such clauses, streamlining compliance reporting.
Another advantage is automated risk flagging, which alerts legal teams to problematic language patterns. This shifts the legal department’s role from operational to strategic, enabling the organization to proactively address compliance issues and avoid liability.
Classification alone is most effective when the focus is on identifying whether specific provisions exist and categorizing them, rather than extracting every detail.
Combining Both Approaches
Using both extraction and classification together combines their strengths, providing comprehensive contract insights. This dual approach is ideal when both operational efficiency and strategic risk management are needed. Extraction identifies provisions, while classification interprets their significance, resulting in complete contract intelligence.
Several factors highlight when to combine these methods. Contract complexity and volume is a major consideration. High-volume, complex portfolios benefit from extraction’s speed paired with classification’s risk analysis. Regulatory requirements in industries with strict compliance standards also call for this combined approach, as classification assesses risks while extraction provides key data for reporting.
Contract diversity across departments is another reason to integrate both methods. For example, a real estate firm may need classification to address tenant rights and subletting provisions, while a software company focuses on IP licensing and data protection clauses. Extraction then pulls specific terms from these classified provisions.
Finally, strategic importance plays a role. Contracts with significant financial or operational impact demand both precise data extraction and risk categorization. This ensures actionable insights by extracting financial data while assessing risk levels.
In this combined approach, extraction lays the groundwork by accurately locating clauses, while classification provides deeper analysis by interpreting their implications. This integration also supports advanced processes like entity extraction and normalization, which require accurate clause detection as a foundation.
However, it’s crucial to note that classification accuracy depends on the quality of extraction. If extraction accuracy is low – say, 75% – classification models will struggle, as errors compound during interpretation. To avoid this, validate your extraction accuracy before adding classification.
A phased rollout reduces risks. Start with extraction for a specific contract type, ensuring key data points are identified with at least 90% accuracy. Once this level is achieved, introduce classification for high-impact clauses like termination, payment terms, and liability limitations.
Measure success through metrics like extraction accuracy, classification precision, time savings compared to manual reviews, and error rates in downstream processes. Many organizations see initial benefits – time savings of 30% to 50% – within 8 to 12 weeks, with full implementation taking 4 to 6 months.
Conclusion
Extraction identifies essential clauses, while classification evaluates their associated risks. Together, these processes transform unstructured legal text into actionable insights, enabling legal teams to manage risks more strategically.
This combined method not only accelerates the review process but also improves risk oversight. For example, automated tools can cut review times from days to just minutes while ensuring better tracking of critical obligations. Reviewing documents can become as effortless as sending an email, requiring minimal training for end users. Teams benefit from complete visibility into contract obligations, renewal schedules, and financial impacts across their entire portfolio.
Trackado leverages AI-driven extraction and classification to eliminate tedious manual data entry, automate reminders, and deliver financial analytics – turning contract management into a streamlined, strategic function. With features like customizable fields, milestone tracking, and task-based approval workflows, the platform centralizes contract storage while providing a clear view of costs, revenues, and important dates.
FAQs
How does combining clause extraction and classification enhance contract management?
Combining clause extraction with clause classification transforms contract management by automating the process of pinpointing key terms, obligations, and potential risks. This means teams can swiftly find the clauses they need, evaluate their significance, and make well-informed decisions – all without the hassle of manual reviews.
Using tools like Trackado, AI-driven data extraction takes the heavy lifting out of these tasks. It saves time, minimizes mistakes, and ensures no critical details slip through the cracks. By integrating these capabilities, businesses can handle contracts more efficiently and stay ahead in managing their obligations.
What do you need to implement clause extraction and classification in legal workflows?
To bring clause extraction and clause classification into your legal workflows, you’ll need a solid Natural Language Processing (NLP) system that can handle the nuances of contract language. Achieving this typically involves:
- High-quality training data: Carefully annotated legal documents are essential to train the AI for pinpoint accuracy in identifying and categorizing clauses.
- Specialized algorithms: Machine learning models tailored to understand the context and intent behind legal clauses.
- Reliable technical infrastructure: A secure and scalable system for processing and storing sensitive legal information while adhering to data protection regulations.
For businesses looking for an easy-to-use solution, tools like Trackado simplify the process. They offer AI-driven data extraction and classification, making contract management more efficient and dependable.
When should a company focus on clause classification instead of clause extraction, and vice versa?
The decision to use clause extraction or clause classification depends entirely on what you need from your contract management process.
If your goal is to pull out specific details – like payment terms, renewal dates, or termination conditions – clause extraction is the way to go. It’s especially handy for turning unstructured contracts into structured data, making it easier to work with and analyze.
On the flip side, clause classification is all about organizing clauses by their purpose or content. For instance, it can help you separate confidentiality clauses from liability clauses. This approach is great for keeping contracts organized, ensuring compliance, or quickly pinpointing clauses that matter for legal or business decisions.
Here’s a quick example: If you’re building a searchable database of contract data, focus on extraction. But if you’re looking to group clauses by type for tasks like risk assessment or reporting, classification will be more useful. In fact, combining both methods can give you the best of both worlds – streamlined processes and deeper insights into your contracts.
