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Found 11 papers, 2 papers with code
BAM: A combination of deep and shallow models for German Dialect Identification.
*This is a submission for the Third VarDial Evaluation Campaign* In this paper, we present a machine learning approach for the German Dialect Identification (GDI) Closed Shared Task of the DSL 2019 Challenge.
A Report on the Third VarDial Evaluation Campaign
In this paper, we present the findings of the Third VarDial Evaluation Campaign organized as part of the sixth edition of the workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with NAACL 2019.
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation
The Vector of Locally-Aggregated Word Embeddings (VLAWE) representation of a document is then computed by accumulating the differences between each codeword vector and each word vector (from the document) associated to the respective codeword.
Ranked #1 on
Sentiment Analysis
on MR
MOROCO: The Moldavian and Romanian Dialectal Corpus
In this work, we introduce the MOldavian and ROmanian Dialectal COrpus (MOROCO), which is freely available for download at https://github. com/butnaruandrei/MOROCO.
Transductive Learning with String Kernels for Cross-Domain Text Classification
Although classifiers for a target domain can be trained on labeled text data from a related source domain, the accuracy of such classifiers is usually lower in the cross-domain setting.
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set
Instead, we use the labels predicted by the classifier in the first training iteration.
UnibucKernel Reloaded: First Place in Arabic Dialect Identification for the Second Year in a Row
Furthermore, our top macro-F1 score (58. 92%) is significantly better than the second best score (57. 59%) in the 2018 ADI Shared Task, according to the statistical significance test performed by the organizers.
Automated essay scoring with string kernels and word embeddings
In this work, we present an approach based on combining string kernels and word embeddings for automatic essay scoring.
Ranked #3 on
Automated Essay Scoring
on ASAP
UnibucKernel: A kernel-based learning method for complex word identification
In this paper, we present a kernel-based learning approach for the 2018 Complex Word Identification (CWI) Shared Task.
ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level.
Ranked #8 on
Word Sense Disambiguation
on SemEval 2007 Task 7
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings
In this paper, we propose a novel approach for text classification based on clustering word embeddings, inspired by the bag of visual words model, which is widely used in computer vision.
