In this paper we describe the development of a doctor-patient dialogue corpus to support a speech-to-speech machine translation effort for English-Persian medical dialogues. The corpus was developed by recording and transcribing English-to-English dialogues between medical students and standardized patients (actors who have been trained to portray illness or injury victims), and then translated...

The Character Sequence Modeling (CSM), typically called the Language Modeling, has not received sufficient attention in the current transliteration research. We discuss the impact of various CSM factors like word granularity, smoothing technique, corpus variation, and word origin on the transliteration accuracy. We demonstrate the importance of CSM by showing that for transliterating into Engli...

In this paper we present a rather novel unsupervised method for part of speech (below POS) disambiguation which has been applied to Persian. This method known as Iterative Improved Feedback (IIF) Model, which is a heuristic one, uses only a raw corpus of Persian as well as all possible tags for every word in that corpus as input. During the process of tagging, the algorithm passes through sever...

The present research is a descriptive corpus-based translation study aiming at pinpointing the patterns of translation into Persian when dealing with English Verb Phrase Ellipsis (VPE). After scrutiny of the strategies applied by Persian translators some regular patterns were drawn, with the exception that the observed translation behavior may be taken as advantageous information for improving ...

PEnTrans is an automatic bidirectional English/ Persian text translator. It contains two main modules, PEnT1,2. PEnT1 translates English sentences into Persian and PEnT2 performs translation from Persian to English. WSD which is an important part in translation is done in both systems by employing a combination of extended dictionary and corpus based approaches in PEnT1 and employing a combinat...

Persian is one of the Indo-European languages which has borrowed its script from Arabic, a member of Semitic language family. Since Persian and Arabic scripts are so similar, problems arise when we want to process an electronic text. In this paper, some of the common problems faced experimentally in developing a corpus for Persian are discussed. The sources of the problems are the Persian scrip...

Abstract Metadiscourse features refer to those elements by which interaction between writer-reader and/or speaker-audience is constructed. Taking this into account, the objective of contrastive parallel corpus-based study was explore way metadiscourse were used and distributed in English discourse their translation Persian language as well analyzing translation. For purpose, 30 different TED ta...

This paper presents the statistical part-ofspeech tagger HunPoS trained on a Persian corpus. The result of the experiments shows that HunPoS provides an overall accuracy of 96.9%, which is the best result reported for Persian part-of-speech tagging.