94 citations · 191 across the 9 of their papers we have counts for
12 papers
Comparison of Current Approaches to Lemmatization: A Case Study in Estonian
Aleksei Dorkin, Kairit Sirts
This study evaluates three different lemmatization approaches to Estonian -- Generative character-level models, Pattern-based word-level classification models, and rule-based morph…
Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources
Kirill Milintsevich, Kairit Sirts
We propose a novel hybrid approach to lemmatization that enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. During…
Evaluating Sentence Segmentation and Word Tokenization Systems on Estonian Web Texts
Kairit Sirts, Kairit Peekman
Texts obtained from web are noisy and do not necessarily follow the orthographic sentence and word boundary rules. Thus, sentence segmentation and word tokenization systems that ha…
EstBERT: A Pretrained Language-Specific BERT for Estonian
Hasan Tanvir, Claudia Kittask, Sandra Eiche +1
This paper presents EstBERT, a large pretrained transformer-based language-specific BERT model for Estonian. Recent work has evaluated multilingual BERT models on Estonian tasks an…
Evaluating Multilingual BERT for Estonian
Claudia Kittask, Kirill Milintsevich, Kairit Sirts
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 Es…
Modeling Composite Labels for Neural Morphological Tagging
Alexander Tkachenko, Kairit Sirts
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. We pr…
Neural Morphological Tagging for Estonian
Alexander Tkachenko, Kairit Sirts
We develop neural morphological tagging and disambiguation models for Estonian. First, we experiment with two neural architectures for morphological tagging - a standard multiclass…
The Impact of Annotation Guidelines and Annotated Data on Extracting App Features from App Reviews
Faiz Ali Shah, Kairit Sirts, Dietmar Pfahl
Annotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact on the quality of machine learning models. In this study, we e…
Idea density for predicting Alzheimer's disease from transcribed speech
Kairit Sirts, Olivier Piguet, Mark Johnson
Idea Density (ID) measures the rate at which ideas or elementary predications are expressed in an utterance or in a text. Lower ID is found to be associated with an increased risk…
Linear Ensembles of Word Embedding Models
Avo Muromägi, Kairit Sirts, Sven Laur
This paper explores linear methods for combining several word embedding models into an ensemble. We construct the combined models using an iterative method based on either ordinary…
STransE: a novel embedding model of entities and relationships in knowledge bases
Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu +1
Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases…
Neighborhood Mixture Model for Knowledge Base Completion
Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu +1
Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mi…