Collective Nouns in Journalistic Arabic

Concord with collective nouns revisited

Automatic Transliteration of Proper Nouns from Arabic to English

After providing a brief introduction to the transliteration problem, and highlighting some issues specific to Arabic to English translation, a three phase algorithm is introduced as a computational solution to the problem. The algorithm is based on a Hidden Markov Model approach, but also leverages information available in on-line databases. The algorithm is then evaluated, and shown to achieve...

Learnability and generalisation of Arabic broken plural nouns

The noun plural system in Modern Standard Arabic lies at a nexus of critical issues in morphological learnability. The suffixing "sound" plural competes with as many as 31 non-concatenative "broken" plural patterns. Our computational analysis of singular-plural pairs in the Corpus of Contemporary Arabic explores what types of linguistic information are statistically relevant to morphological ge...

Count Nouns – Mass Nouns Neat Nouns – Mess Nouns

In this paper I propose and formalize a theory of the mass-count distinction in which the denotations of count nouns are built from non-overlapping generators, while the denotations of mass nouns are built from overlapping generators. Counting is counting of generators, and it will follow that counting is only correct on count denotations. I will show that the theory allows two kinds of mass no...

Proper Nouns Recognition in Arabic Crime Text Using Machine Learning Approach

Named Entity Recognition (NER) identifies proper nouns in a text and categorizes it as a distinct kind of named entities. This function enables the extraction of peoples name, locations, organizations, and currencies. Several research abound in this area in Arabic NER is concerned. However, recognizing Arabic named entities is challenging due to the complexity in the Arabic language. These comp...