Computational Linguistics
Computational linguistics applies computational methods to analyze and understand human language, aiming to build systems that can process and generate human-like text. Current research focuses on leveraging large language models (LLMs) for tasks like semantic change detection, sentiment analysis, and even literary analysis, alongside the development of new datasets and benchmark tasks for under-resourced languages. This field is crucial for advancing natural language processing applications across diverse sectors, from improving user experience on digital platforms to facilitating more nuanced analyses of historical texts and literary works.
Papers
ACL Anthology Helper: A Tool to Retrieve and Manage Literature from ACL Anthology
Chen Tang, Frank Guerin, Chenghua Lin
ChiSCor: A Corpus of Freely Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science
Bram M. A. van Dijk, Max J. van Duijn, Suzan Verberne, Marco R. Spruit
Words That Stick: Predicting Decision Making and Synonym Engagement Using Cognitive Biases and Computational Linguistics
Nimrod Dvir, Elaine Friedman, Suraj Commuri, Fan Yang, Jennifer Romano
A Predictive Model of Digital Information Engagement: Forecasting User Engagement With English Words by Incorporating Cognitive Biases, Computational Linguistics and Natural Language Processing
Nimrod Dvir, Elaine Friedman, Suraj Commuri, Fan yang, Jennifer Romano
The ACL OCL Corpus: Advancing Open Science in Computational Linguistics
Shaurya Rohatgi, Yanxia Qin, Benjamin Aw, Niranjana Unnithan, Min-Yen Kan
A Distributed Automatic Domain-Specific Multi-Word Term Recognition Architecture using Spark Ecosystem
Ciprian-Octavian Truică, Neculai-Ovidiu Istrate, Elena-Simona Apostol