activity
20212026
most citedA Syntax-Injected Approach for Faster and More Accurate Sentiment Analysis

2 citations · 4 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CL2026

SLA-LENS: From Classroom Transcripts to CSLA-Informed and Evidence-Linked L2 Interaction Analytics

Nemika Tyagi, Olga Kellert, Phanos Kareen +1

What happens after a learner makes an error, and how does the instructor's response shape the next turn? Investigating these exchanges across classrooms requires context-sensitive…

cs.CL2026

Structured Disagreement in Health-Literacy Annotation: Epistemic Stability, Conceptual Difficulty, and Agreement-Stratified Inference

Olga Kellert, Sriya Kondury, Candice Koo +2

Annotation pipelines in Natural Language Processing (NLP) commonly assume a single latent ground truth per instance and resolve disagreement through label aggregation. Perspectivis…

cs.CL2026

Lost in Speech: Benchmarking, Evaluation, and Parsing of Spoken Bilingual Conversational Language Beyond Standard UD Assumptions

Nemika Tyagi, Olga Kellert, Holly Hendrix +7

Spoken bilingual conversations pose substantial challenges for syntactic parsing because they often include disfluencies and discourse-driven structures that complicate dependency…

cs.CL2025

Modeling Topics and Sociolinguistic Variation in Code-Switched Discourse: Insights from Spanish-English and Spanish-Guaraní

Nemika Tyagi, Nelvin Licona Guevara, Olga Kellert

This study presents an LLM-assisted annotation pipeline for the sociolinguistic and topical analysis of bilingual discourse in two typologically distinct contexts: Spanish-English…

cs.CL2025

Evaluating Compositional Approaches for Focus and Sentiment Analysis

Olga Kellert, Muhammad Imran, Nicholas Hill Matlis +2

This paper summarizes the results of evaluating a compositional approach for Focus Analysis (FA) in Linguistics and Sentiment Analysis (SA) in Natural Language Processing (NLP). Wh…

cs.CL2025

Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages

Olga Kellert, Nemika Tyagi, Muhammad Imran +2

Code-switching presents a complex challenge for syntactic analysis, especially in low-resource language settings where annotated data is scarce. While recent work has explored the…

cs.CL2024

Dancing in the syntax forest: fast, accurate and explainable sentiment analysis with SALSA

Carlos Gómez-Rodríguez, Muhammad Imran, David Vilares +2

Sentiment analysis is a key technology for companies and institutions to gauge public opinion on products, services or events. However, for large-scale sentiment analysis to be acc…

cs.CL2024★ 2 cited

A Syntax-Injected Approach for Faster and More Accurate Sentiment Analysis

Muhammad Imran, Olga Kellert, Carlos Gómez-Rodríguez

Sentiment Analysis (SA) is a crucial aspect of Natural Language Processing (NLP), focusing on identifying and interpreting subjective assessments in textual content. Syntactic pars…

cs.CL2024

Unveiling factors influencing judgment variation in Sentiment Analysis with Natural Language Processing and Statistics

Olga Kellert, Carlos Gómez-Rodríguez, Mahmud Uz Zaman

TripAdvisor reviews and comparable data sources play an important role in many tasks in Natural Language Processing (NLP), providing a data basis for the identification and classif…

cs.CL2023★ 2 cited

Experimenting with UD Adaptation of an Unsupervised Rule-based Approach for Sentiment Analysis of Mexican Tourist Texts

Olga Kellert, Mahmud Uz Zaman, Nicholas Hill Matlis +1

This paper summarizes the results of experimenting with Universal Dependencies (UD) adaptation of an Unsupervised, Compositional and Recursive (UCR) rule-based approach for Sentime…

cs.CL2021

Geolocation differences of language use in urban areas

Olga Kellert, Nicholas H. Matlis

The explosion in the availability of natural language data in the era of social media has given rise to a host of applications such as sentiment analysis and opinion mining. Simult…