Search Results for author:
Found 38 papers, 3 papers with code
AIR-JPMC@SMM4H’22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models
This paper presents our submission for the SMM4H 2022-Shared Task on the classification of self-reported intimate partner violence on Twitter (in English).
AIR-JPMC@SMM4H’22: BERT + Ensembling = Too Cool: Using Multiple BERT Models Together for Various COVID-19 Tweet Identification Tasks
This paper presents my submission for Tasks 1 and 2 for the Social Media Mining of Health (SMM4H) 2022 Shared Tasks competition.
ViziTex: Interactive Visual Sense-Making of Text Corpora
Information visualization is critical to analytical reasoning and knowledge discovery.
AIR-JPMC@SMM4H’22: Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling
We present our response to Task 5 of the Social Media Mining for Health Applications (SMM4H) 2022 competition.
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.
Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency
Collecting labeled datasets in finance is challenging due to scarcity of domain experts and higher cost of employing them.
Log Summarisation for Defect Evolution Analysis
Log analysis and monitoring are essential aspects in software maintenance and identifying defects.
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.
Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams
Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art task-specific models.
Synthetic Text Generation using Hypergraph Representations
Generating synthetic variants of a document is often posed as text-to-text transformation.
Unsupervised Domain Adaptation using Lexical Transformations and Label Injection for Twitter Data
A large body of literature tries to solve this problem by adapting models trained on the source domain to the target domain.
How Effective Are Neural Networks for Fixing Security Vulnerabilities
The results call for innovations to enhance automated Java vulnerability repair such as creating larger vulnerability repair training data, tuning LLMs with such data, and applying code simplification transformation to facilitate vulnerability repair.
InProC: Industry and Product/Service Code Classification
However, unavailability of labeled datasets as well as the need for high precision results within the financial domain makes this a challenging problem.
REFinD: Relation Extraction Financial Dataset
A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment.
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks
The most recent large language models(LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation.
Ranked #1 on
Question Answering
on ConvFinQA
Bayesian Hierarchical Models for Counterfactual Estimation
Counterfactual explanations utilize feature perturbations to analyze the outcome of an original decision and recommend an actionable recourse.
Neural Transition-based Parsing of Library Deprecations
This paper tackles the challenging problem of automating code updates to fix deprecated API usages of open source libraries by analyzing their release notes.
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching.
Ranked #2 on
Question Answering
on ConvFinQA
AIR-JPMC@SMM4H'22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models
This paper presents our submission for the SMM4H 2022-Shared Task on the classification of self-reported intimate partner violence on Twitter (in English).
Online Learning for Mixture of Multivariate Hawkes Processes
Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors.
Bandit Sampling for Multiplex Networks
Most of the existing work focuses primarily on the monoplex setting where we have access to a network with only a single type of connection between entities.
Structure and Semantics Preserving Document Representations
Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text.
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training
Our study of 22 mitigation techniques and five baselines reveals up to 12. 6% fairness variance across identical training runs with identical seeds.
Synthetic Document Generator for Annotation-free Layout Recognition
Analyzing the layout of a document to identify headers, sections, tables, figures etc.
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.
FinQA: A Dataset of Numerical Reasoning over Financial Data
In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations.
Ranked #4 on
Question Answering
on FinQA
Debiasing classifiers: is reality at variance with expectation?
We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse rather than better.
Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering
One of the key characteristics of these applications is the wide range of strategies that an adversary may choose as they adapt their strategy dynamically to sustain benefits and evade authorities.
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.
Explicit Group Sparse Projection with Applications to Deep Learning and NMF
Instead, in our approach we set the sparsity level for the whole set explicitly and simultaneously project a group of vectors with the sparsity level of each vector tuned automatically.
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 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.
Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits
Previous studies have shown that investor sentiment indicators can predict stock market change.
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.
