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Document Review: Popular TAR Platforms and Algorithmic Insights

There are several popular tools available in the market for Technology Assisted Review (TAR), each with its unique features and capabilities. Here are some of the most widely recognized TAR tools:


  • Relativity Assisted Review: Relativity Assisted Review is a TAR solution offered within the Relativity eDiscovery platform. It combines advanced analytics and machine learning to facilitate efficient document review. The tool provides various features for training the system, validating results, and iterative improvement of the TAR process.

  • Brainspace: Brainspace is a TAR platform that utilizes AI-driven analytics and visualizations to help users quickly analyze and make sense of large volumes of data. It offers features like concept clustering, predictive coding, and workflow management, enabling users to uncover insights and identify relevant documents effectively.

  • Catalyst Insight: Catalyst Insight is a TAR tool that incorporates advanced analytics and machine learning to streamline document review and improve accuracy. It provides features such as continuous active learning, concept searching, and integrated analytics to assist in the TAR process.

  • NexLP: NexLP is an AI-powered TAR tool that specializes in analyzing unstructured data to identify relevant information and patterns. It offers advanced linguistic and behavioral analytics, natural language processing (NLP) capabilities, and machine learning algorithms to assist in document review and investigation tasks.

  • Everlaw: Everlaw is an eDiscovery platform that includes a TAR module called StoryBuilder. It combines machine learning with intuitive workflows to facilitate efficient document review and predictive coding. Everlaw's TAR tool provides visualizations, analytics, and collaboration features to enhance the review process.

  • OpenText Axcelerate: OpenText Axcelerate is an eDiscovery and TAR platform that utilizes advanced analytics and AI-driven workflows to streamline document review and analysis. It offers features such as technology-assisted review, concept clustering, near-duplicate detection, and predictive coding to enhance the efficiency and accuracy of the review process.


Technology-Assisted Review (TAR) software utilize various algorithms to perform its tasks. Here are some commonly used algorithms in TAR:


  • Continuous Active Learning (CAL): CAL is an iterative process that involves selecting a subset of documents for review and using the feedback from human reviewers to train the TAR model. The model then suggests additional documents for review based on the updated knowledge gained from previous review iterations.

  • Support Vector Machines (SVM): SVM is a machine learning algorithm commonly used in TAR. It classifies documents into relevant and non-relevant categories based on a set of features extracted from the documents. SVM aims to find the optimal hyperplane that separates the relevant and non-relevant documents.

  • Naive Bayes: Naive Bayes is a probabilistic algorithm that calculates the probability of a document belonging to a specific category. It assumes that the features are independent of each other, which simplifies the calculations. Naive Bayes is often used in text classification tasks, including document categorization in TAR.

  • Decision Trees: Decision trees are hierarchical structures that make decisions based on a series of conditions. In TAR, decision trees can be used to classify documents based on their features. The tree structure is built by recursively splitting the data based on the most informative features.

  • Random Forests: Random forests are an ensemble learning method that combines multiple decision trees. Each tree is trained on a different subset of the data, and the final prediction is made based on the majority vote of all the trees. Random forests are known for their robustness and ability to handle high-dimensional data.

  • Neural Networks: Neural networks, particularly deep learning models, have gained popularity in TAR. These models consist of multiple layers of interconnected nodes (neurons) that mimic the structure of the human brain. Neural networks can learn complex patterns and relationships in the data, making them effective for tasks like document classification and relevance ranking.

  • Logistic Regression: Logistic regression is a statistical algorithm used to model the relationship between input variables and a binary outcome. It is commonly used in TAR for document classification tasks, where the goal is to determine the relevance of documents based on their features.

  • K-Nearest Neighbors (KNN): KNN is a non-parametric algorithm that classifies new data points based on the majority vote of their nearest neighbors in the feature space. In TAR, KNN can be used to classify documents based on their similarity to previously reviewed documents.

  • Latent Semantic Analysis (LSA): LSA is a technique that analyzes relationships between documents and terms in a corpus to uncover hidden semantic structures. It can be used in TAR to identify and group documents with similar thematic content.

  • Latent Dirichlet Allocation (LDA): LDA is a probabilistic topic modeling algorithm that assigns documents to a mixture of topics based on the distribution of words within the documents. It is useful in TAR for identifying key topics or themes within a document collection.

  • Genetic Algorithms: Genetic algorithms are optimization algorithms inspired by the process of natural selection. In TAR, they can be used to evolve and refine the parameters or feature sets used by other machine learning algorithms to improve their performance.

  • Deep Reinforcement Learning: Deep reinforcement learning combines deep learning with reinforcement learning principles. It can be applied in TAR to optimize the review process by learning from interactions between reviewers and the system, effectively adapting to evolving needs and improving efficiency.


Technology Assisted Review revolutionizes the document review process by harnessing the power of AI and machine learning. By automating document categorization and prioritization, TAR accelerates the review process, improves accuracy, reduces costs, and offers transparency and defensibility. As organizations continue to face the challenge of managing and analyzing vast volumes of information, TAR emerges as a crucial tool in their arsenal, enabling more efficient and effective decision-making in the realm of legal, regulatory, and investigative endeavors.

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