Computational Linguistics and Information Processing (CLIP)

The CLIP Laboratory at the University of Maryland is engaged in designing algorithms and methods that allow computers to effectively and efficiently perform human language-related tasks.  CLIP is using computational methods to improve our scientific understanding of the human capacity for language and to explore heterogeneous datasets at scale; for example, how to design an online search engine that searches content across human languages?

CLIP research covers major areas of computational research on language, including deep learning, human-in-the-loop machine learning, multilingual text processing and low-resource languages, machine translation, and summarization. We also study speech retrieval and cross-language information retrieval when performing online searches. Additional challenges include ranking and personalization, computational social science for mental health, data science for finance, data science for social good, computational psycholinguistics, and e-discovery.

With faculty, researchers, and students spanning the Department of Computer Science, the Department of Linguistics, the iSchool, and the Robert H. Smith School of Business, we are a group known not only for high-quality research, but also for intellectual diversity and strong collaborations.

  CLIP Laboratory Website

Research Projects

IARPA BETTER: Multilingual Fine-grained Decompositional Analysis
Principal Investigator(s): Jordan Boyd-Graber
Funder: USODNI Intelligence Advanced Research Projects Activity
Research Areas: Computational Linguistics, Machine Learning, and Information Retrieval
Developing enhanced methods for personalized, multilingual semantic extraction and retrieval from text, in support of IARPA's goal of providing users with a system that quickly and accurately extracts complex semantic information, targeted for a specific user, from text.

Recent News

A map of Europe with game pieces of military equipment and ships placed sporadically on top of it.

Can a Strategy Game Help AI Learn to Spot Scammers?

April 27, 2022 | Maria Herd
iSchool Associate Professor, Dr. Jordan Boyd-Graber, joins team of researchers awarded $1M to support U.S. Military’s cybersecurity fight.
graphic of gears with elements inside of them like an @ symbol, magnifying glass, paper clip, and anchor

iSchool’s New Interest Group Explores Advancements in Search Mastery and Education

April 5, 2021 | Hayleigh Moore Maria Herd
iSchool Associate Professor, Dr. Jordan Boyd-Graber, joins team of researchers awarded $1M to support U.S. Military’s cybersecurity fight.