In this work, we present an approach based on combining string kernels and word
embeddings for automatic essay scoring. String kernels capture the similarity among strings …

A common approach in text mining tasks such as text categorization, authorship
identification or plagiarism detection is to rely on features like words, part-of-speech tags …

Networks-on-chip (NoCs) have recently emerged as a scalable alternative to classical bus
and point-to-point architectures. To date, performance evaluation of NoC designs is largely …

Ensemble methods using multiple classifiers have proven to be among the most successful
approaches for the task of Native Language Identification (NLI), achieving the current state …

We present a machine learning approach for the Arabic Dialect Identification (ADI) and the
German Dialect Identification (GDI) Closed Shared Tasks of the DSL 2017 Challenge. The …

The most common approach in text mining classification tasks is to rely on features like
words, part-of-speech tags, stems, or some other high-level linguistic features. Recently, an …

Motivated by the seemingly high accuracy levels of machine learning (ML) models in
Moldavian versus Romanian dialect identification and the increasing research interest on …

The prediction of an author's native language using only their second language writing—a
task called Native Language Identification (NLI)—is usually tackled using supervised …

The most common approach in text mining classification tasks is to rely on features like
words, part-of-speech tags, stems, or some other high-level linguistic features. Unlike the …

Machine learning is currently a vast area of research with applications in a broad range of
fields such as computer vision, bioinformatics, information retrieval, natural language …