Search Results for author:
Found 19 papers, 5 papers with code
Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset
This paper addresses the quality of annotations in mental health datasets used for NLP-based depression level estimation from social media texts.
Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources
We also compare with other methods of integrating external data into lemmatization and show that our enhanced system performs considerably better than a simple lexicon extension method based on the Stanza system, and it achieves complementary improvements w. r. t.
Evaluating Sentence Segmentation and Word Tokenization Systems on Estonian Web Texts
Texts obtained from web are noisy and do not necessarily follow the orthographic sentence and word boundary rules.
EstBERT: A Pretrained Language-Specific BERT for Estonian
This paper presents EstBERT, a large pretrained transformer-based language-specific BERT model for Estonian.
Evaluating Multilingual BERT for Estonian
Recently, large pre-trained language models, such as BERT, have reached state-of-the-art performance in many natural language processing tasks, but for many languages, including Estonian, BERT models are not yet available.
Modeling Composite Labels for Neural Morphological Tagging
Neural morphological tagging has been regarded as an extension to POS tagging task, treating each morphological tag as a monolithic label and ignoring its internal structure.
Neural Morphological Tagging for Estonian
Secondly, we complement these models with the analyses generated by a rule-based Estonian morphological analyser (MA) VABAMORF , thus performing a soft morphological disambiguation.
The Impact of Annotation Guidelines and Annotated Data on Extracting App Features from App Reviews
Our experiments show that having annotated training reviews from the test app is not necessary although including them into training set helps to improve recall.
Idea density for predicting Alzheimer's disease from transcribed speech
ID has been used in two different versions: propositional idea density (PID) counts the expressed ideas and can be applied to any text while semantic idea density (SID) counts pre-defined information content units and is naturally more applicable to normative domains, such as picture description tasks.
Linear Ensembles of Word Embedding Models
The results show that while using the ordinary least squares regression performs poorly in our experiments, using orthogonal Procrustes to combine several word embedding models into an ensemble model leads to 7-10% relative improvements over the mean result of the initial models in synonym tests and 19-47% in analogy tests.
STransE: a novel embedding model of entities and relationships in knowledge bases
Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.
Neighborhood Mixture Model for Knowledge Base Completion
Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete.
Minimally-Supervised Morphological Segmentation using Adaptor Grammars
This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation.
