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Found 8 papers, 4 papers with code
Text classification with word embedding regularization and soft similarity measure
In our work, we investigate the individual and joint effect of the two word embedding regularization techniques on the document processing speed and the task performance of the SCM and the WMD on text classification.
Ranked #2 on
Document Classification
on Amazon
One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages
In our work, we find the optimal subword sizes on the English, German, Czech, Italian, Spanish, French, Hindi, Turkish, and Russian word analogy tasks.
EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses
We report evaluation results on 11 benchmark datasets involving WSD and Word Similarity tasks and show that our method for enhancing distributional semantic structures improves embeddings quality on the baselines.
When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting
In 2018, Mikolov et al. introduced the positional language model, which has characteristics of attention-based neural machine translation models and which achieved state-of-the-art performance on the intrinsic word analogy task.
WebMIaS on Docker: Deploying Math-Aware Search in a Single Line of Code
Math informational retrieval (MIR) search engines are absent in the wide-spread production use, even though documents in the STEM fields contain many mathematical formulae, which are sometimes more important than text for understanding.
Regressive Ensemble for Machine Translation Quality Evaluation
This work introduces a simple regressive ensemble for evaluating machine translation quality based on a set of novel and established metrics.
Adaptor: Objective-Centric Adaptation Framework for Language Models
Progress in natural language processing research is catalyzed by the possibilities given by the widespread software frameworks.
People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts
Although pre-trained named entity recognition (NER) models are highly accurate on modern corpora, they underperform on historical texts due to differences in language OCR errors.
