Search Results for author: Nedim Lipka

Found 32 papers, 14 papers with code

Knowledge Graph Prompting for Multi-Document Question Answering

1 code implementation22 Aug 2023 Yu Wang, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, Tyler Derr

Concurrently, the graph traversal agent acts as a local navigator that gathers pertinent context to progressively approach the question and guarantee retrieval quality.

graph construction Open-Domain Question Answering +1

LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

1 code implementation29 Jun 2023 Yanzhe Zhang, Ruiyi Zhang, Jiuxiang Gu, Yufan Zhou, Nedim Lipka, Diyi Yang, Tong Sun

Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans.

16k Image Captioning +3

Envisioning the Next-Gen Document Reader

1 code implementation15 Feb 2023 Catherine Yeh, Nedim Lipka, Franck Dernoncourt

People read digital documents on a daily basis to share, exchange, and understand information in electronic settings.

A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings

no code implementations28 Dec 2022 Ryan Aponte, Ryan A. Rossi, Shunan Guo, Jane Hoffswell, Nedim Lipka, Chang Xiao, Gromit Chan, Eunyee Koh, Nesreen Ahmed

In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph.

Hyperedge Prediction Node Classification +1

Graph Learning with Localized Neighborhood Fairness

no code implementations22 Dec 2022 April Chen, Ryan Rossi, Nedim Lipka, Jane Hoffswell, Gromit Chan, Shunan Guo, Eunyee Koh, Sungchul Kim, Nesreen K. Ahmed

Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node.

Fairness Graph Learning +2

Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs

no code implementations30 Sep 2022 Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim, Tong Yu, Jane Hoffswell, Nedim Lipka, Shunan Guo, Cameron Musco

We present a simple method that avoids both shortcomings: construct the line graph of the network, which includes a node for each interaction, and weigh the edges of this graph based on the difference in time between interactions.

Edge Classification Link Prediction

Survey of Aspect-based Sentiment Analysis Datasets

1 code implementation11 Apr 2022 Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, Thamar Solorio

Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews to determine: a) The target entity being reviewed, b) The high-level aspect to which it belongs, and c) The sentiment expressed toward the targets and the aspects.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)

Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes

no code implementations29 Nov 2021 Aravind Reddy, Ryan A. Rossi, Zhao Song, Anup Rao, Tung Mai, Nedim Lipka, Gang Wu, Eunyee Koh, Nesreen Ahmed

In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory.

Point Processes valid

Exploring Conditional Text Generation for Aspect-Based Sentiment Analysis

1 code implementation5 Oct 2021 Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, Thamar Solorio

Aspect-based sentiment analysis (ABSA) is an NLP task that entails processing user-generated reviews to determine (i) the target being evaluated, (ii) the aspect category to which it belongs, and (iii) the sentiment expressed towards the target and aspect pair.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1

StreamHover: Livestream Transcript Summarization and Annotation

1 code implementation EMNLP 2021 Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh, Fei Liu

With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge.

Extractive Summarization

Syntopical Graphs for Computational Argumentation Tasks

no code implementations ACL 2021 Joe Barrow, Rajiv Jain, Nedim Lipka, Franck Dernoncourt, Vlad Morariu, Varun Manjunatha, Douglas Oard, Philip Resnik, Henning Wachsmuth

Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection.

Stance Detection

Developing a Conversational Recommendation System for Navigating Limited Options

no code implementations13 Apr 2021 Victor S. Bursztyn, Jennifer Healey, Eunyee Koh, Nedim Lipka, Larry Birnbaum

We have developed a conversational recommendation system designed to help users navigate through a set of limited options to find the best choice.


Learning to Emphasize: Dataset and Shared Task Models for Selecting Emphasis in Presentation Slides

no code implementations2 Jan 2021 Amirreza Shirani, Giai Tran, Hieu Trinh, Franck Dernoncourt, Nedim Lipka, Paul Asente, Jose Echevarria, Thamar Solorio

We evaluate a range of state-of-the-art models on this novel dataset by organizing a shared task and inviting multiple researchers to model emphasis in this new domain.

Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning

no code implementations1 Jan 2021 Jun Yan, Mrigank Raman, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren

Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.

Knowledge Graphs Natural Language Inference +1

Graph Neural Networks with Heterophily

1 code implementation28 Sep 2020 Jiong Zhu, Ryan A. Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra

Graph Neural Networks (GNNs) have proven to be useful for many different practical applications.

SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media

no code implementations SEMEVAL 2020 Amirreza Shirani, Franck Dernoncourt, Nedim Lipka, Paul Asente, Jose Echevarria, Thamar Solorio

In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media.


ISA: An Intelligent Shopping Assistant

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Tuan Manh Lai, Trung Bui, Nedim Lipka

Despite the growth of e-commerce, brick-and-mortar stores are still the preferred destinations for many people.

Let Me Choose: From Verbal Context to Font Selection

2 code implementations ACL 2020 Amirreza Shirani, Franck Dernoncourt, Jose Echevarria, Paul Asente, Nedim Lipka, Thamar Solorio

In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to.

Towards Summarization for Social Media - Results of the TL;DR Challenge

no code implementations WS 2019 Shahbaz Syed, Michael V{\"o}lske, Nedim Lipka, Benno Stein, Hinrich Sch{\"u}tze, Martin Potthast

In this paper, we report on the results of the TL;DR challenge, discussing an extensive manual evaluation of the expected properties of a good summary based on analyzing the comments provided by human annotators.

Towards Open Intent Discovery for Conversational Text

no code implementations17 Apr 2019 Nikhita Vedula, Nedim Lipka, Pranav Maneriker, Srinivasan Parthasarathy

Existing research for intent discovery model it as a classification task with a predefined set of known categories.

Intent Discovery Open Intent Discovery

Supervised Transfer Learning for Product Information Question Answering

no code implementations8 Jan 2019 Tuan Manh Lai, Trung Bui, Nedim Lipka, Sheng Li

Popular e-commerce websites such as Amazon offer community question answering systems for users to pose product related questions and experienced customers may provide answers voluntarily.

Community Question Answering Transfer Learning

Task Proposal: The TL;DR Challenge

no code implementations WS 2018 Shahbaz Syed, Michael V{\"o}lske, Martin Potthast, Nedim Lipka, Benno Stein, Hinrich Sch{\"u}tze

The TL;DR challenge fosters research in abstractive summarization of informal text, the largest and fastest-growing source of textual data on the web, which has been overlooked by summarization research so far.

Abstractive Text Summarization Information Retrieval +1

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