Machine Learning
WitnessTree ML is a Machine Learning-based, advanced concept classification software that has the ability to classify human behavior patterns expressed within digital media. This includes Emails, Instant Messages, Social Networks and virtually any ESI that falls under direct human-to-human written communication.
WitnessTree ML has the ability to learn patterns of human communication and classify text with extreme prejudice. WitnessTree ML uses a Learning Engine that can be trained using a representative sample. Once trained, it uses an Opinion Mining Engine for classification of documents that are responsive to the search.
Opinion Mining or Sentiment Analysis is an area of specialization within Natural Language Processing, Computational Linguistics and Text mining. Opinion Mining is the use of statistical models and software for the identification and classification of the opinion (attitude) of an opinion holder on a given subject. The classification categorizes the attitude of an opinion holder as supporting, opposing or neutral to a given subject.
Opinion Mining is a proven technology and has been used in recent times for a wide variety of applications in Defense, Government and Marketing applications as a listening, analysis and engagement tool.
WitnessTree ML uses a sophisticated Opinion Mining algorithm to extract, isolate and classify the opinions expressed in a document a by opinion holder(s), within the data set, with a high level of accuracy. The result is a reduced set of responsive documents that are classified as those supporting, opposing and neutral to, the opinion holder’s sentiment about an identified subject.
WitnessTree ML analyzes text at a sentence level and returns as output, the identified sentence, along with the document. This effect is similar to a human reviewer accepting or rejecting a given document based on a query.
WitnessTree ML can be trained using an Intensive or Incremental process.
Intensive training is applicable in cases that involve very large corpus where responsive data is known to be present in large quantities. Witness ML can be trained to identify and classify data based on behavior.
Incremental training can be used by law firms that provide specialist services concerning human behavior. Representative sample data from cases that are unrelated but similar in context e.g. several different sexual harassment cases where the context is sexual harassment but the cases are unrelated.
Once WitnessTree ML has been trained to an acceptable level of accuracy (typically 80-85%), WitnessTree ML can operate as a standalone software for data classification in the next case. This is sone in a supervised mode with process-driven checks and balances to ensure completeness and accuracy.
WitnessTree ML is a useful tool for law firms that provide sepcialist services in areas such as sexual harassment or bias-related cases, where the difference between success and failure depends upon more intuitive behavior pattern identification.
For more information on how WitnessTree ML can help your organization make the eDiscovery process more efficient, please contact us at 623-242-2979 or send us an email at info@jadefalconit.com.
