We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech
tagging. The key hypothesis of multilingual learning is that by combining cues from multiple …

There has been a lot of recent interest in the natural language processing (NLP) community
in the computational processing of language varieties and dialects, with the aim to improve …

Indian languages are known to have a large speaker base, yet some of these languages
have minimal or non-efficient linguistic resources. For example, Kannada is relatively …

Natural language processing (NLP) aims, broadly speaking, to teach computers to
understand human language. This is hard as the computer must comprehend many facets of …

This paper reports the principles behind designing a tagset to cover Russian
morphosyntactic phenomena, modifications of the core tagset, and its evaluation. The tagset …

As the Internet and World Wide Web have continued to gain widespread adoption, the
linguistic diversity represented has also been growing. Simultaneously the field of …

We present the first empirical study of distinguishing Persian and Dari texts at the sentence
level, using discriminative models. As Dari is a low-resourced language, we developed a …

We present an unsupervised approach to part-of-speech tagging based on projections of
tags in a word-aligned bilingual parallel corpus. In contrast to the existing state-of-the-art …

In this study we apply classification methods for detecting subdialectal differences in Sorani
Kurdish texts produced in different regions, namely Iran and Iraq. As Sorani is a low …

We investigate the problem of unsupervised part-of-speech tagging when raw parallel data
is available in a large number of languages. Patterns of ambiguity vary greatly across …