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Found 18 papers, 1 papers with code
Where is this coming from? Making groundedness count in the evaluation of Document VQA models
Document Visual Question Answering (VQA) models have evolved at an impressive rate over the past few years, coming close to or matching human performance on some benchmarks.
"What is the value of {templates}?" Rethinking Document Information Extraction Datasets for LLMs
In this work, we present K2Q, a diverse collection of five datasets converted from KIE to a prompt-response format using a plethora of bespoke templates.
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law: domains characterized by their reliance on professional expertise, challenging data acquisition, high-stakes, and stringent regulatory compliance.
BuDDIE: A Business Document Dataset for Multi-task Information Extraction
Several datasets exist for research on specific tasks of VRDU such as document classification (DC), key entity extraction (KEE), entity linking, visual question answering (VQA), inter alia.
TreeForm: End-to-end Annotation and Evaluation for Form Document Parsing
Visually Rich Form Understanding (VRFU) poses a complex research problem due to the documents' highly structured nature and yet highly variable style and content.
DocGraphLM: Documental Graph Language Model for Information Extraction
Advances in Visually Rich Document Understanding (VrDU) have enabled information extraction and question answering over documents with complex layouts.
DocLLM: A layout-aware generative language model for multimodal document understanding
Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalities.
Parameterized Explanations for Investor / Company Matching
We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task.
A Framework for Institutional Risk Identification using Knowledge Graphs and Automated News Profiling
Organizations around the world face an array of risks impacting their operations globally.
Robust Document Representations using Latent Topics and Metadata
Task specific fine-tuning of a pre-trained neural language model using a custom softmax output layer is the de facto approach of late when dealing with document classification problems.
DocuBot : Generating financial reports using natural language interactions
Digital reports are often created based on tedious manual analysis as well as visualization of the underlying trends and characteristics of data.
SPot: A tool for identifying operating segments in financial tables
In this paper we present SPot, an automated tool for detecting operating segments and their related performance indicators from earnings reports.
A framework for anomaly detection using language modeling, and its applications to finance
In the finance sector, studies focused on anomaly detection are often associated with time-series and transactional data analytics.
Reuters Tracer: Toward Automated News Production Using Large Scale Social Media Data
It has a bottom-up approach to news detection, and does not rely on a predefined set of sources or subjects.
Social and Information Networks
Data Sets: Word Embeddings Learned from Tweets and General Data
In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets with the general data.
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts
This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter.
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter
This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs.
