Results 111 to 120 of about 4,133 (219)

Multi-label text classification via secondary use of large clinical real-world data sets

Scientific Reports
Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural language processing offer a promising avenue for developing applications that assist clinicians, thereby alleviating their administrative burdens.
Sai Pavan Kumar Veeranki, Akhila Abdulnazar, Diether Kramer, Markus Kreuzthaler, David Benjamin Lumenta   +4 more
doaj   +1 more source

Non-Contextual BERT or FastText? A Comparative Analysis

Proceedings of the Workshop on Beyond English: Natural Language Processing for all Languages in an Era of Large Language Models
Natural Language Processing (NLP) for low-resource languages, which lack large annotated datasets, faces significant challenges due to limited high-quality data and linguistic resources. The selection of embeddings plays a critical role in achieving strong performance in NLP tasks.
Shanbhag, Abhay, Jadhav, Suramya, Thakurdesai, Amogh, Sinare, Ridhima, Joshi, Raviraj   +4 more
openaire   +2 more sources

Kubord-fasttext - Dagens Nyheter 2010–2024 - token

Kubord-fasttext is a collection of fasttext models, developed within a collaboration between KBLab and Språkbanken Text, that have been trained on the same underlying data as Kubord 2. The models have been trained on the token and the lemma level.
https://ror.org/03xfh2n14
core   +1 more source

Kubord-fasttext - Göteborgsposten 2013–2024 - token

Kubord-fasttext is a collection of fasttext models, developed within a collaboration between KBLab and Språkbanken Text, that have been trained on the same underlying data as Kubord 2. The models have been trained on the token and the lemma level.
https://ror.org/03xfh2n14
core   +1 more source

NCHLT Afrikaans fastText-Skipgram embeddings

, 2023
Static word and subword embeddings for the Skipgram flavour of the fastText architecture (Bojanowski et al., 2017).
Roald Eiselen
core  

Kubord-fasttext - Göteborgsposten 2013–2022 - lemma

Kubord-fasttext is a collection of fasttext models, developed within a collaboration between KBLab and Språkbanken Text, that have been trained on the same underlying data as Kubord 2. The models have been trained on the token and the lemma level.
https://ror.org/03xfh2n14
core   +1 more source

Semantic-BERT and semantic-FastText model for education question classification

IJAIN (International Journal of Advances in Intelligent Informatics)
Question classification (QC) is critical in an educational question-answering (QA) system. However, most existing models suffer from limited semantic accuracy, particularly when dealing with complex or ambiguous education queries.
Teotino Gomes Soares, Azhari Azhari, Nur Rohkman   +2 more
doaj   +1 more source

Kubord-fasttext - Dagens Nyheter 2010–2022 - token

Kubord-fasttext is a collection of fasttext models, developed within a collaboration between KBLab and Språkbanken Text, that have been trained on the same underlying data as Kubord 2. The models have been trained on the token and the lemma level.
https://ror.org/03xfh2n14
core   +1 more source

Towards AI-Generated Essay Classification Using Numerical Text Representation

Applied Sciences
The detection of essays written by AI compared to those authored by students is increasingly becoming a significant issue in educational settings. This research examines various numerical text representation techniques to improve the classification of ...
Natalia Krawczyk, Barbara Probierz, Jan Kozak   +2 more
doaj   +1 more source

Kubord-fasttext - Aftonbladet 2010–2024 - lemma

Kubord-fasttext is a collection of fasttext models, developed within a collaboration between KBLab and Språkbanken Text, that have been trained on the same underlying data as Kubord 2. The models have been trained on the token and the lemma level.
https://ror.org/03xfh2n14
core   +1 more source