4 citations · 10 across the 5 of their papers we have counts for
6 papers
Transformer-based Model for Word Level Language Identification in Code-mixed Kannada-English Texts
Atnafu Lambebo Tonja, Mesay Gemeda Yigezu, Olga Kolesnikova +3
Using code-mixed data in natural language processing (NLP) research currently gets a lot of attention. Language identification of social media code-mixed text has been an interesti…
CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Texts
H. L. Shashirekha, F. Balouchzahi, M. D. Anusha +1
The task of automatically identifying a language used in a given text is called Language Identification (LI). India is a multilingual country and many Indians especially youths are…
PolyHope: Two-Level Hope Speech Detection from Tweets
Fazlourrahman Balouchzahi, Grigori Sidorov, Alexander Gelbukh
Hope is characterized as openness of spirit toward the future, a desire, expectation, and wish for something to happen or to be true that remarkably affects human's state of mind,…
The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation
Tadesse Destaw Belay, Atnafu Lambebo Tonja, Olga Kolesnikova +5
Machine translation (MT) is one of the main tasks in natural language processing whose objective is to translate texts automatically from one natural language to another. Nowadays,…
Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences
Hoang Thang Ta, Alexander Gelbukha, Grigori Sidorov
Acknowledged as one of the most successful online cooperative projects in human society, Wikipedia has obtained rapid growth in recent years and desires continuously to expand cont…
Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF
Ignacio Arroyo-Fernández, Carlos-Francisco Méndez-Cruz, Gerardo Sierra +2
Sentence representation at the semantic level is a challenging task for Natural Language Processing and Artificial Intelligence. Despite the advances in word embeddings (i.e. word…