In this work, we introduce the Moldavian and Romanian Dialectal Corpus (MOROCO), which is freely available for download at https://github.com/butnaruandrei/MOROCO. The corpus contains 33564 samples of text (with over 10 million tokens) collected from the news domain.The samples belong to one of the following six topics: culture, finance, politics, science, sports and tech.The data set is divided into 21719 samples for training, 5921 samples for validation and another 5924 samples for testing.For each sample, we provide corresponding dialectal and category labels.This allows us to perform empirical studies on several classification tasks such as (i) binary discrimination of Moldavian versus Romanian text samples, (ii) intra-dialect multi-class categorization by topic and (iii) cross-dialect multi-class categorization by topic.We perform experiments using a shallow approach based on string kernels, as well as a novel deep approach based on character-level convolutional neural networks containing Squeeze-and-Excitation blocks.We also present and analyze the most discriminative features of our best performing model, before and after named entity removal.

Recently, string kernels have obtained stateof-the-art results in various text classification tasks such as Arabic dialect identification or native language identification.In this paper, we apply two simple yet effective transductive learning approaches to further improve the results of string kernels.The first approach is based on interpreting the pairwise string kernel similarities between samples in the training set and samples in the test set as features.Our second approach is a simple self-training method based on two learning iterations.In the first iteration, a classifier is trained on the training set and tested on the test set, as usual.In the second iteration, a number of test samples (to which the classifier associated higher confidence scores) are added to the training set for another round of training.However, the ground-truth labels of the added test samples are not necessary.Instead, we use the labels predicted by the classifier in the first training iteration.By adapting string kernels to the test set, we report significantly better accuracy rates in English polarity classification and Arabic dialect identification.

In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level.Our algorithm is inspired by a widely-used approach in the field of genetics for whole genome sequencing, known as the Shotgun sequencing technique.The proposed WSD algorithm is based on three main steps.First, a brute-force WSD algorithm is applied to short context windows (up to 10 words) selected from the document in order to generate a short list of likely sense configurations for each window.In the second step, these local sense configurations are assembled into longer composite configurations based on suffix and prefix matching.The resulted configurations are ranked by their length, and the sense of each word is chosen based on a voting scheme that considers only the top k configurations in which the word appears.We compare our algorithm with other state-of-the-art unsupervised WSD algorithms and demonstrate better performance, sometimes by a very large margin.We also show that our algorithm can yield better performance than the Most Common Sense (MCS) baseline on one data set.Moreover, our algorithm has a very small number of parameters, is robust to parameter tuning, and, unlike other bioinspired methods, it gives a deterministic solution (it does not involve random choices).

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. After each word in a collection of documents is represented as word vector using a pre-trained word embeddings model, a k-means algorithm is applied on the word vectors in order to obtain a fixed-size set of clusters. The centroid of each cluster is interpreted as a super word embedding that embodies all the semantically related word vectors in a certain region of the embedding space. Every embedded word in the collection of documents is then assigned to the nearest cluster centroid. In the end, each document is represented as a bag of super word embeddings by computing the frequency of each super word embedding in the respective document. We also diverge from the idea of building a single vocabulary for the entire collection of documents, and propose to build class-specific vocabularies for better performance. Using this kind of representation, we report results on two text mining tasks, namely text categorization by topic and polarity classification. On both tasks, our model yields better performance than the standard bag of words.