Nedim Lipka

Sr. Research Scientist

San Jose

Dr. Nedim Lipka is a senior research scientist at the Big Data Experience Lab of Adobe Research. His main research interests are in statistical machine learning from volatile web data, especially, from user-generated content and user behavior.

He is passionate about research in the field of Natural Language Understanding with applications in conversational services.

Prior to joining Adobe in November 2012, he worked as a research associate at the Web Technology and Information Systems group at the Bauhaus-Universität Weimar, Germany, where he developed expertise in data mining, machine learning, and text classification. He has been published at several international conferences (e.g., ICDM, WWW, CIKM, SIGIR, ECIR, CLEF) and serves frequently in the program committees of NLP and IR conferences. Most of the publications can be found here.

Publications

OATS: A Challenge Dataset for Opinion Aspect Target Sentiment Joint Detection for Aspect-Based Sentiment Analysis

Chebolu, S., Dernoncourt, F., Lipka, N., Solorio, T. (May. 25, 2024)

LREC-COLING 2024

A Review of Datasets for Aspect-based Sentiment Analysis

Chebolu, S., Dernoncourt, F., Lipka, N., Solorio, T. (Nov. 4, 2023)

IJCNLP-AACL 2023

TaleStream: Supporting Story Ideation with Trope Knowledge

Chou, J., Agrawala, M., Siu, A., Lipka, N., Rossi, R., Dernoncourt, F. (Nov. 1, 2023)

UIST 2023

How Small Businesses Transform PDF Agreements into Action

So, J., Lipka, N., Siu, A., Rossi, R., Dernoncourt, F. (Oct. 18, 2023)

CSCW 2023 Posters

Envisioning the Next-Gen Document Reader

Yeh, C., Lipka, N., Dernoncourt, F. (Feb. 14, 2023)

AAAI-23 Workshop on Scientific Document Understanding

A Framework for Knowledge-Derived Query Suggestions

Rezayi, S., Lipka, N., Vinay, V., Rossi, R., Dernoncourt, F., King, T., Li, S. (Dec. 17, 2021)

2021 IEEE International Conference on Big Data

StreamHover: Livestream Transcript Summarization and Annotation

Cho, S., Dernoncourt, F., Ganter, T., Bui, T., Lipka, N., Chang, W., Jin, H., Brandt, J., Foroosh, H., Liu, F. (Nov. 9, 2021)

Empirical Methods in Natural Language Processing (EMNLP)

Exploring Conditional Text Generation for Aspect-based Sentiment Analysis

Chebolu‬, S., Dernoncourt, F., Lipka, N., Solorio, T. (Nov. 7, 2021)

PACLIC 2021

PSED: A Dataset for Selecting Emphasis in Presentation Slides

Shirani, A., Tran, G., Trinh, H., Dernoncourt, F., Lipka, N., Echevarria, J., Solorio, T., Asente, P. (Aug. 4, 2021)

ACL 2021 Findings

Syntopical Graphs for Computational Argumentation Tasks

Barrow, J., Jain, R., Lipka, N., Dernoncourt, F., Morariu, V., Manjunatha, V., Oard, D., Resnik, P., Wachsmuth, H. (Aug. 4, 2021)

ACL 2021

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

Shirani, A., Tran, G., Trinh, H., Dernoncourt, F., Lipka, N., Asente, P., Echevarria, J., Solorio, T. (Feb. 8, 2021)

AAAI 2021 Workshop on Content Authoring and Design (CAD21)

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

Shirani, A., Dernoncourt, F., Lipka, N., Asente, P., Echevarria, J., Solorio, T. (Dec. 13, 2020)

SemEval 2020

Let Me Choose: From Verbal Context to Font Selection

Shirani, A., Dernoncourt, F., Echevarria, J., Asente, P., Lipka, N., Solorio, T. (Jul. 8, 2020)

ACL 2020

Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions

Shirani, A., Dernoncourt, F., Asente, P., Lipka, N., Kim, S., Echevarria, J., Solorio, T. (Aug. 2, 2019)

ACL 2019

Supervised Transfer Learning for Product Information Question Answering

Lai, T., Bui, T., Lipka, N., Li, S. (Dec. 17, 2018)

Proc. of ICMLA

Conversion Prediction from Clickstream: Modeling Market Prediction and Customer Predictability

Yeo, J., Hwang, S., Kim, S., Koh, E., Lipka, N. (Nov. 30, 2018)

IEEE Transactions on Knowledge and Data Engineering