Science

We believe that science and technology are advanced through the ongoing, free dissemination of research and best practices. Zuva, in part, has been built on the work of others and as technology leaders, we continue the tradition of sharing our research.


Building a Better Mousetrap: Tools and Processes for Selling A Company

In an increasingly competitive funding market, current technologic offerings often fall short of providing optimal support to the start-up and buyer and reinforce a process that is cumbersome and chaotic.

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Spectator: An Open Source Document Viewer

Building a high-quality document viewer often exceeds the resources of researchers. In this paper, we describe the design of our open-source document viewer.

Redesigning Document Viewer for Legal Documents

This paper reports on the user-focused redesign of our document viewer that is used by clients to review documents and train machine learning algorithms to find pertinent information from these contracts.

On Tradeoffs Between Document Signature Methods for a Legal Due Diligence Corpus

We present an examination of the tradeoffs that document signature methods face in the due diligence domain. In particular, we quantify the trade-off between signature length, time to compute, number of hash collisions, and number of nearest neighbours for a 90,000 document due diligence corpus.

On Interpretability and Feature Representations: An Analysis of the Sentiment Neuron

We are concerned with investigating the apparent effective-ness of Radford et al.'s "Sentiment Neuron," which they claim encapsulates sufficient knowledge to accurately predict sentiment in reviews. In our analysis of the Sentiment Neuron, we find that the removal of the neuron only marginally affects a classifier's ability to detect and label sentiment and may even improve performance.

From Bubbles to Lists: Designing Clustering for Due Diligence

In an effort to aid users to review more efficiently, we sought to determine how document-level clustering may help users of a due diligence system during their workflow.

Dancing with the AI Devil: Investigating the Partnership Between Lawyers and AI

As professional users interact with more AI-enabled tools, it has become increasingly important to understand how their work and behaviour are affected by such tools. In this paper, we present the insights that we have gleaned from a qualitative user study conducted with nine of our software's users who are all legal professionals.

Automatic and Semi-Automatic Document Selection for Technology-Assisted Review

We are concerned with investigating the apparent effective-ness of Radford et al.'s "Sentiment Neuron," which they claim encapsulates sufficient knowledge to accurately predict sentiment in reviews. In our analysis of the Sentiment Neuron, we find that the removal of the neuron only marginally affects a classifier's ability to detect and label sentiment and may even improve performance.

A Reliable and Accurate Multiple Choice Question Answering System for Due Diligence

The problem of answering multiple choice questions, based on the content of documents has been studied extensively in the machine learning literature. We pose the due diligence problem, where lawyers study legal contracts and assess the risk in potential mergers and acquisitions, as a multiple choice question answering problem, based on the text of the contract.

A Dataset and an Examination of Identifying Passages for Due Diligence

We present and formalize the due diligence problem, where lawyers extract data from legal documents to assess risk in a potential merger or acquisition, as an information retrieval task.