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Project Management

Improving Construction Contract Review with AI: NLP and ML

  • Introduction

Construction contracts are critical governance instruments that delineate the scope, payments, responsibilities, and dispute resolution processes between employers and contractors. These documents communicate the employer's expectations and requirements, making them essential for successful project delivery. However, contracts can become sources of risk if they contain ambiguities, unclear allocations of responsibility, or if parties are unaware of the contractual conditions. Manual review of these extensive documents requires substantial expertise and effort, often constrained by limited bidding periods, which can lead to overlooked risks and potential disputes. This underscores the need for automated systems that can rapidly and accurately analyze contract texts with minimal manual intervention. This literature review examines the existing research on the use of Natural Language Processing (NLP) and Machine Learning (ML) to automate the review of construction contracts, focusing on risk and responsibility assessment.

  • Main Themes in Automated Construction Contract Review

The primary focus of automated contract review in the construction industry revolves around several key themes:

  • Risk and Responsibility Assessment: A central goal is to automatically identify clauses related to risk, responsibility, and rights, which are critical for formulating risk management strategies and plans. Contracts are a significant driver of risk premiums in the construction sector, making their thorough review essential.
  • Information Extraction: Researchers have leveraged NLP and ML to extract key contractual elements such as parties, duration, and governing law. This enables a more structured analysis of contract terms.
  • Text Classification: A common approach involves categorizing contract sentences into predefined taxonomies. This includes classifying sentences by type (e.g., Heading, Definition, Obligation, Risk, Right) and by the related parties (e.g., Contractor, Employer, Shared).
  • Ambiguity and Compliance Checking: Another vital area is detecting ambiguous clauses, identifying modifications to standard contract forms, and ensuring compliance with regulations. Ambiguities can lead to conflicts and disputes, thus requiring meticulous analysis.

III. Points of Agreement and Debate in the Research

The existing body of research shows several points of agreement, while also revealing areas of debate:

  • Agreement:
  • There is a consensus on the necessity for automated contract review to expedite the process and improve accuracy, addressing the time constraints of bidding processes.
  • Researchers agree on the utility of NLP and ML techniques for processing textual contract data, which allows for more efficient analysis.
  • The value of using standard contract templates, such as those from the International Federation of Consulting Engineers (FIDIC), for training and testing models is widely recognized.
  • Debate:
  • There is an ongoing debate about the effectiveness of different NLP techniques. Some studies use rule-based approaches, while others focus on machine learning; this includes variations in word embeddings (e.g., custom Word2Vec, spaCy, GloVe, BERT).
  • The optimal approach for classifying contract clauses is still being explored, with some research employing multi-class classification while others use binary classification.
  • The balance between using custom and pre-trained word embeddings is another point of discussion. Custom embeddings are trained on specific datasets, while pre-trained embeddings leverage large, general-purpose corpora.
  • Methodologies Used in the Studies

The methodologies used in the research include several common steps:

  • Data Preparation: This involves converting contract documents from PDF to text using libraries such as Python PDFMiner. The text is then cleaned to remove extraneous artifacts like heading breaks, page numbers, and watermarks. Sentence extraction is performed using NLP libraries like spaCy, and complex sentences are algorithmically broken down.
  • Dataset Labeling: Manual annotation of sentences into categories and assignment of related parties. This is a critical step in supervised learning, and expert review is sometimes used to validate the categorization.
  • NLP Techniques:
  • Text Vectorization: Methods like Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) are used to convert text into numerical data for machine learning.
  • Word Embeddings: Various pre-trained and custom word embeddings (e.g., Word2Vec, GloVe, BERT) are employed to capture semantic relationships between words.
  • Machine Learning Algorithms:
  • Supervised Learning Algorithms: Common algorithms include Logistic Regression, Support Vector Machines (SVM), Decision Trees, Recurrent Neural Networks (RNNs), and BERT.
  • Ensemble Methods: Techniques like competitive voting are used to combine the predictions of multiple models for improved performance.
  • Performance Evaluation:
  • Metrics: Accuracy, precision, recall, and F1 scores are used to evaluate the performance of the models.
  • Validation Procedures: Expert review, and binary classification are some of the validation procedures employed.
  • Development in the Field Over Time

The field of automated contract review has evolved over time:

  • Early studies focused on requirements engineering and ambiguity detection. These studies aimed to improve the quality of requirements documents by identifying and resolving ambiguities using NLP techniques.
  • There was a shift towards automated contract management, including compliance checking and information extraction. Research began to focus on automating laborious manual tasks, like extracting key contract elements and checking compliance with regulations.
  • More recent research emphasizes risk assessment using advanced NLP and ML. The focus has shifted towards using AI to identify clauses related to risk and responsibility, providing critical inputs for risk management.
  • The emergence of deep learning methods, particularly transformer models like BERT, has significantly improved the contextual understanding of contract text. These models have shown superior performance compared to traditional statistical learning methods.
  • Current work is focusing on enhancing the automated contract review process to extract information on risk, responsibility, and rights, with allocated parties. This includes developing models that can identify shared risks and responsibilities, which can be used to analyze specific clauses.
  • Critical Insights and Contextualization
  • Alignment: The studies align in their overall goal of automating contract review to support risk management and bid preparation activities. They demonstrate a progression from basic NLP techniques to more advanced deep learning methods.
  • Divergence: Research diverges in the specific methods employed, such as the choice of text vectorization, machine learning algorithms, and classification approaches (multi-class vs. binary). Some studies concentrate on specific aspects, like ambiguity or compliance checking, while others take a broader approach to risk assessment.
  • Gaps:
  • There is a need for more diverse datasets beyond FIDIC contracts to ensure that models can generalize to other types of agreements.
  • The limited exploration of rule-based methods alongside ML algorithms represents a gap, as combining these could improve overall performance.
  • More work is needed on handling ambiguous or complex clauses, which are often the root of disputes.
  • There is a need to compare the performance of fine-tuned BERT models and other Large Language Models(LLMs) like GPT to determine their effectiveness for contract review.
  • Overall Contribution: Each research paper contributes to the development of automated systems that enhance risk management by rapidly identifying clauses related to risk and its allocation. This is particularly helpful given the time constraints in the construction sector. The progression in research shows the growing sophistication of AI techniques for analyzing complex legal documents.

VII. Conclusion

This literature review shows that automated construction contract review using NLP and ML has made significant advancements. The application of AI techniques such as BERT has demonstrated substantial improvements in accuracy and efficiency compared to traditional methods. The integration of binary classification and ensemble methods has further enhanced the performance of these systems. However, challenges remain, particularly regarding the need for more diverse datasets and the handling of ambiguous language. Future research should explore the combination of rule-based and machine learning approaches, as well as compare the performance of fine-tuned BERT models with other LLMs. The ultimate goal is to create automated systems that can reliably analyze construction contracts, enabling quicker and more informed decisions, and reducing the risk of disputes.

(Dikmen et al., 2025)

Reference:

Dikmen, I., Eken, G., Erol, H., & Birgonul, M. T. (2025). Automated construction contract analysis for risk and responsibility assessment using natural language processing and machine learning. Computers in Industry, 166. https://doi.org/10.1016/j.compind.2025.104251

Frequently Asked Questions: Automated Construction Contract Analysis

  • Why is automated contract analysis necessary in the construction industry? Construction contracts are complex documents containing critical information about risk, responsibilities, and rights. Manually reviewing these extensive documents is time-consuming, requires expertise, and is prone to errors, especially given the often-limited bidding periods. Overlooking key clauses can lead to disputes and financial risks during project execution. Automated systems can quickly analyze contract texts, identifying crucial information and risk allocation, supporting risk management and preventing disputes.
  • What are the main categories used to classify sentences in construction contracts for this study? Sentences are classified using two primary taxonomies: sentence types and related parties. Sentence types include "Heading," "Definition," "Obligation," "Risk," and "Right." The related parties are "Contractor," "Employer," and "Shared," indicating who is primarily affected by or responsible for the obligation, risk, or right described in the sentence. Headings and Definitions do not have a related party, and related party assignment is only made for Obligation, Right, and Risk.
  • How were the contract documents prepared for analysis in this research? The contract documents, initially in PDF format, were transformed into analyzable text using Python libraries. This included extracting text from the PDFs, cleaning extraneous characters (like page numbers and watermarks), splitting the text into sentences, and further rearranging complex sentences into self-contained clauses based on syntactic rules. These processed sentences were then organized into Excel files, ready for labeling and machine learning.
  • What is the "binary classification" approach used in this study, and why was it implemented? The "binary classification" approach divides the multi-class problem (classifying into more than 2 categories) into multiple binary classification tasks. This research applied it to sentence types by grouping categories (e.g., first distinguishing "Heading" from "all other categories, then "Definition" from the rest, etc.). This breakdown simplifies the classification process, allowing models to learn more specific characteristics of each sentence type and improve accuracy.
  • What machine learning (ML) algorithms were employed, and how were they combined with NLP techniques? The study employed various combinations of ML algorithms and Natural Language Processing (NLP) techniques. The ML algorithms included Logistic Regression, Support Vector Machine (SVM), Decision Tree, Recurrent Neural Networks (RNN), and Bidirectional Encoder Representations from Transformers (BERT). These were coupled with text vectorization methods such as Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), spaCy word embeddings, custom word embeddings (using Word2Vec), GloVe word embeddings, and BERT word embeddings.
  • What is "competitive voting" as an ensemble method, and what advantages does it offer? The "competitive voting" ensemble method combines predictions from the top-performing models by selecting the prediction which had the strongest support among the models. The models were selected based on their external test results on the specific classification task (either sentence type or related party). This ensemble method leverages the strengths of different models, often improving overall performance and robustness compared to using a single model alone, by counterbalancing individual model's limitations.
  • What were the key performance metrics used to evaluate the models, and what results were achieved? The performance of the models was evaluated using metrics like accuracy, precision, recall, and the F1 score. The initial results were improved using binary classification and the competitive voting ensemble method. The best performance was achieved by the BERT model after ensemble method, attaining an accuracy of 89% and an F1 score of 86% for sentence type classification and an accuracy of 83% and an F1 score of 76% for related party classification, with the use of ensemble method.
  • What are the potential limitations of this research, and what future research directions are suggested? One limitation is that the training data primarily uses FIDIC contracts, thus limiting generalizability to other contract forms. The manual labeling process and category selections introduce potential subjectivity. Future work should aim to create more comprehensive training datasets covering various types of contracts and possibly include rule-based methods, ambiguity detection, and reinforcement learning to expand the applicability and robustness of the models. Furthermore, comparative analysis of other LLMs such as RoBERTa, ELECTRA, DeBERTa and LEGAL-BERT against the models used in this research is also recommended to further evaluate the performance and applicability of LLMs in this domain