3 citations · 6 across the 17 of their papers we have counts for
7 papers · 1 filter
TartuNLP @ AXOLOTL-24: Leveraging Classifier Output for New Sense Detection in Lexical Semantics
Aleksei Dorkin, Kairit Sirts
We present our submission to the AXOLOTL-24 shared task. The shared task comprises two subtasks: identifying new senses that words gain with time (when comparing newer and older ti…
Context is Important in Depressive Language: A Study of the Interaction Between the Sentiments and Linguistic Markers in Reddit Discussions
Neha Sharma, Kairit Sirts
Research exploring linguistic markers in individuals with depression has demonstrated that language usage can serve as an indicator of mental health. This study investigates the im…
TartuNLP at EvaLatin 2024: Emotion Polarity Detection
Aleksei Dorkin, Kairit Sirts
This paper presents the TartuNLP team submission to EvaLatin 2024 shared task of the emotion polarity detection for historical Latin texts. Our system relies on two distinct approa…
Sõnajaht: Definition Embeddings and Semantic Search for Reverse Dictionary Creation
Aleksei Dorkin, Kairit Sirts
We present an information retrieval based reverse dictionary system using modern pre-trained language models and approximate nearest neighbors search algorithms. The proposed appro…
Evaluating Lexicon Incorporation for Depression Symptom Estimation
Kirill Milintsevich, Gaël Dias, Kairit Sirts
This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Lexicon informati…
TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages
Aleksei Dorkin, Kairit Sirts
We present our submission to the unconstrained subtask of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages for morphological annotation…
Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset
Kirill Milintsevich, Kairit Sirts, Gaël Dias
This paper addresses the quality of annotations in mental health datasets used for NLP-based depression level estimation from social media texts. While previous research relies on…