While document signatures are a well established tool in IR, they have primarily been investigated in the context of web documents. Legal due diligence documents, by their nature, have more similar structure and language than we may expect out of standard web collections. Moreover, many due diligence systems strive to facilitate real-time interactions and so time from document ingestion to availability should be minimal. Such constraints further limit the possible solution space when identifying near duplicate documents. 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.Read the Paper
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Redesigning Document Viewer for Legal Documents
In Mergers and Acquisition due diligence, lawyers are tasked with analyzing a collection of contracts and determine the level of risk that comes from a merger or acquisition. This process has historically been manual and resulted in only a small fraction of the collection being examined. 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 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. Moreover, the effectiveness of the Sentiment Neuron can be surpassed by simply using 100 random neurons as features to the same classifier.