Navneet Potti
Navneet Potti completed his PhD in Data Management in University of Wisconsin - Madison in 2018, under the guidance of Prof. Jignesh Patel. During his graduate study, he worked on various aspects of building high-performance data management systems, as well as intuitive user interfaces for data analytics. His thesis research is being commercialized at a startup, DataChat. His current research focuses on information extraction from text documents. In the past, he worked as a quantitative analyst for Goldman Sachs, and did internships at IBM Almaden Research Center, Pivotal and Google. He holds a BTech and an MTech in Electrical Engineering from Indian Institute of Technology - Madras.
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Selective Labeling: How to Radically Lower Data-Labeling Costs for Document Extraction Models
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, ACL, pp. 3847-3860
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Building automatic extraction models for visually rich documents like invoices, receipts, bills, tax forms, etc. has received significant attention lately. A key bottleneck in developing extraction models for new document types is the cost of acquiring the several thousand high-quality labeled documents that are needed to train a model with acceptable accuracy. In this paper, we propose selective labeling as a solution to this problem. The key insight is to simplify the labeling task to provide “yes/no” labels for candidate extractions predicted by a model trained on partially labeled documents. We combine this with a custom active learning strategy to find the predictions that the model is most uncertain about. We show through experiments on document types drawn from 3 different domains that selective labeling can reduce the cost of acquiring labeled data by 10× with a negligible loss in accuracy.
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Data-Efficient Information Extraction from Form-Like Documents
Document Intelligence Workshop @ KDD 2021
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Automating information extraction from form-like documents at scale is a pressing need due to its potential impact on automating business workflows across many industries like financial services, insurance, and healthcare. The key challenge is that form-like documents in these business workflows can be laid out in virtually infinitely many ways; hence, a good solution to this problem should generalize to documents with unseen layouts and languages. A solution to this problem requires a holistic understanding of both the textual segments and the visual cues within a document, which is non-trivial. While the natural language processing and computer vision communities are starting to tackle this problem, there has not been much focus on (1) data-efficiency, and (2) ability to generalize across different document types and languages.
In this paper, we show that when we have only a small number of labeled documents for training (~50), a straightforward transfer learning approach from a considerably structurally-different larger labeled corpus yields up to a 27 F1 point improvement over simply training on the small corpus in the target domain. We improve on this with a simple multi-domain transfer learning approach, that is currently in production use, and show that this yields up to a further 8 F1 point improvement. We make the case that data efficiency is critical to enable information extraction systems to scale to handle hundreds of different document-types, and learning good representations is critical to accomplishing this.
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Glean: Structured Extractions from Templatic Documents
Proceedings of the VLDB Endowment (2021), pp. 997-1005
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Extracting structured information from templatic documents is an important problem with the potential to automate many real-world business workflows such as payment, procurement, and payroll. The core challenge is that such documents can be laid out in virtually infinitely different ways. A good solution to this problem is one that generalizes well not only to known templates such as invoices from a known vendor, but also to unseen ones.
We developed a system called Glean to tackle this problem. Given a target schema for a document type and some labeled documents of that type, Glean uses machine learning to automatically extract structured information from other documents of that type. In this paper, we describe the overall architecture of Glean, and discuss three key data management challenges : 1) managing the quality of ground truth data, 2) generating training data for the machine learning model using labeled documents, and 3) building tools that help a developer rapidly build and improve a model for a given document type. Through empirical studies on a real-world dataset, we show that these data management techniques allow us to train a model that is over 5 F1 points better than the exact same model architecture without the techniques we describe. We argue that for such information-extraction problems, designing abstractions that carefully manage the training data is at least as important as choosing a good model architecture.
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Representation Learning for Information Extraction from Form-like Documents
Bodhisattwa Majumder
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 6495-6504
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We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images. We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases.
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Hidden in Plain Sight: Classifying Emails Using Embedded Image Contents
Proceedings of the 2018 World Wide Web Conference (WWW 2018), pp. 1865-1874
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A vast majority of the emails received by people today are machine-generated by businesses communicating with consumers. While some emails originate as a result of a transaction (e.g., hotel or restaurant reservation confirmations, online purchase receipts, shipping notifications, etc.), a large fraction are commercial emails promoting an offer (a special sale, free shipping, available for a limited time, etc.). The sheer number of these promotional emails makes it difficult for users to read all these emails and decide which ones are actually interesting and actionable. In this paper, we tackle the problem of extracting information from commercial emails promoting an offer to the user. This information enables an email platform to build several new experiences that can unlock the value in these emails without the user having to navigate and read all of them. For instance, we can highlight offers that are expiring soon, or display a notification when there's an unexpired offer from a merchant if your phone recognizes that you are at that merchant's store.
A key challenge in extracting information from such commercial emails is that they are often image-rich and contain very little text. Training a machine learning (ML) model on a rendered image-rich email and applying it to each incoming email can be prohibitively expensive. In this paper, we describe a cost-effective approach for extracting signals from both the text and image content of commercial emails in the context of a free email platform that serves over a billion users around the world. The key insight is to leverage the template structure of emails, and use off-the-shelf OCR techniques to obtain the text from images to augment the existing text features offline. Compared to a text-only approach, we show that we are able to identify 9.12% more email templates corresponding to ~5% more emails being identified as offers. Interestingly, our analysis shows that this 5% improvement in coverage is across the board, irrespective of whether the emails were sent by large merchants or small local merchants, allowing us to deliver an improved experience for everyone.
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