Brown clustering is an established technique, used in hundreds of computational linguistics
papers each year, to group word types that have similar distributional information. It is …

Distribution information plays an important role in word categorization. In this paper, we
present a novel distributional model, distributional lattices to discover syntactic categories in …

This thesis addresses the problem of fine-grained NER for Arabic, which poses unique
linguistic challenges to NER; such as the absence of capitalisation and short vowels, the …

One of the most fundamental tasks in natural language processing is representing words
with mathematical objects (such as vectors). The word representations, which are most often …

Word representations induced from models with discrete latent variables (eg\HMMs) have
been shown to be beneficial in many NLP applications. In this work, we exploit labeled …

Over the last decade, Natural Language Processing (NLP) technologies have entered
mainstream usage. Machine Translation, Intelligent Virtual Assistants like Siri and Alexa …

There is ample research in natural language processing (NLP) on obtaining word
representations, including vector space modeling, clustering and techniques derived from …