Many information retrieval tasks require viewing documents in some manner, whether this is to view information in context or to provide annotations for some downstream task (e.g., evaluation or system training). Building a high-quality document viewer often exceeds the resources of many researchers and so, in this paper, we describe the design and architecture of our new open-source document viewer, Spectator. In particular, we provide a look into the algorithmic details of how Spectator accomplishes tasks like mapping annotations back to the canonical document. Moreover, we provide a sampling of the use cases that we envision for Spectator, potential future additions depending on community need and support, and highlight situations where Spectator may not be a good fit. Furthermore, we provide a brief description of the sample application that we bundle with Spectator to demonstrate how one might use it within the context of a larger system.
Read the PaperInterested in hearing more from Zuva?
Read more papers
Science
The Utility of Context When Extracting Entities From Legal Documents
When reviewing documents for legal tasks such as Mergers and Acquisitions, granular information (such as start dates and exit clauses) need to be identified and extracted. Inspired by previous work in Named Entity Recognition (NER), we investigate how NER techniques can be leveraged to aid lawyers in this review process. Due to the extremely low prevalence of target information in legal documents, we find that the traditional approach of tagging all sentences in a document is inferior, in both effectiveness and data required to train and predict, to using a first-pass layer to identify sentences that are likely to contain the relevant information and then running the more traditional sentence-level sequence tagging.
Science
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.