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Found 5 papers, 4 papers with code
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work.
The Challenge of Diacritics in Yoruba Embeddings
The major contributions of this work include the empirical establishment of a better performance for Yoruba embeddings from undiacritized (normalized) dataset and provision of new analogy sets for evaluation.
Corpora Compared: The Case of the Swedish Gigaword & Wikipedia Corpora
In this work, we show that the difference in performance of embeddings from differently sourced data for a given language can be due to other factors besides data size.
Exploring Swedish & English fastText Embeddings for NER with the Transformer
To achieve a good network performance in natural language processing (NLP) downstream tasks, several factors play important roles: dataset size, the right hyper-parameters, and well-trained embeddings.
Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks
However, wrong combination of hyper-parameters can produce poor quality vectors.
