Communication tools make the world like a small village, and as a consequence people can contact with others who are from different societies or who speak different languages. This communication cannot happen effectively without Machine-Translation because they can be found anytime and everywhere. There are a number of studies that have developed Machine-Translation for the English language wit...

We consider the task of tagging Arabic nouns with WordNet supersenses. Three approaches are evaluated. The first uses an expertcrafted but limited-coverage lexicon, Arabic WordNet, and heuristics. The second uses unsupervised sequence modeling. The third and most successful approach uses machine translation to translate the Arabic into English, which is automatically tagged with English superse...

In this paper, we present a novel morphology preprocessing technique for ArabicEnglish translation. We exploit the Arabic morphology-English alignment to learn a model removing nonaligned Arabic morphemes. The model is an instance of the Conditional Random Field (Lafferty et al., 2001) model; it deletes a morpheme based on the morpheme’s context. We achieved around two BLEU points improvement o...

In this paper, we describe an extension to a hybrid machine translation system for handling dialect Arabic, using a decoding algorithm to normalize non-standard, spontaneous and dialectal Arabic into Modern Standard Arabic. We prove the feasibility of the approach by measuring and comparing machine translation results in terms of BLEU with and without the proposed approach. We show in our tests...

Translation of named entities (NEs), such as person names, organization names and location names is crucial for cross lingual information retrieval, machine translation, and many other natural language processing applications. Newly named entities are introduced on daily basis in newswire and this greatly complicates the translation task. Named Entities translation between languages having diff...

We carried out a study on monolingual translators with no knowledge of the source language, but aided by post-editing and the display of translation options. On Arabic-English and Chinese-English, using standard test data and current statistical machine translation systems, 10 monolingual translators were able to translate 35% of Arabic and 28% of Chinese sentences correctly on average, with so...

In TREC-10 the Berkeley group participated only in the English-Arabic cross-language retrieval (CLIR) track. One Arabic monolingual run and four English-Arabic cross-language runs were submitted. Our approach to the cross-language retrieval was to translate the English topics into Arabic using online EnglishArabic bilingual dictionaries and machine translation software. The five official runs a...

Cross Language Information Retrieval (CLIR) systems are a valuable tool to enable speakers of one language to search for content of interest expressed in a different language. A group for whom this is of particular interest is bilingual Arabic speakers who wish to search for English language content using information needs expressed in Arabic queries. A key challenge in CLIR is crossing the lan...