In this paper we investigate unsupervised name transliteration using comparable corpora, corpora where texts in the two languages deal in some of the same topics — and therefore share references to named entities — but are not translations of each other. We present two distinct methods for transliteration, one approach using an unsupervised phonetic transliteration method, and the other using t...

Current mobile devices do not support Bangla (or Bengali) Input method. Due to this many Bangla language speakers have to write Bangla in mobile phone using English alphabets. During this time they used to write English foreign words using English spelling. This tendency also exists when writing in computer using phonetically input methods, which cause many typing mistakes. In this scenario, co...

Transliteration is the process of replacing the characters in one language with the corresponding phonetically equivalent characters of the other language. India is a language diversified country where people speak and understand many languages but does not know the script of some of these languages. Transliteration plays a major role in such cases. Transliteration has been a supporting tool in...

This research paper describes a corpus based transliteration system for Punjabi language. The existence of two scripts for Punjabi language has created a script barrier between the Punjabi literature written in India and in Pakistan. This research project has developed a new system for the first time of its kind for Shahmukhi script of Punjabi language. The proposed system for Shahmukhi to Gurm...

This paper describes our cross-language information retrieval system for the NTCIR-4 CLIR task. Our system, which follows the query translation approach, uses a compound word translation and transliteration. Transliteration is effective if a query includes foreign words, such as technical terms and proper nouns, spelled out by phonetic alphabets. We apply our method, which was originally propos...

Machine transliteration has received significant research attention in last two decades. It is observed that Hindi to English and Marathi to English named entity machine transliteration is comparably less studied. Currently, research work in this domain is carried out by using grapheme based statistical approaches. But, to achieve better accuracy for the transliteration, an adequate bilingual t...

Most foreign names are transliterated into Chinese, Japanese or Korean with approximate phonetic equivalents. The transliteration is usually achieved through intermediate phonemic mapping. This paper presents a new framework that allows direct orthographical mapping (DOM) between two different languages, through a joint source-channel model, also called n-gram transliteration model (TM). With t...

One problem seriously affecting CLIR performance is the processing of queries with embedded foreign names. A proper noun dictionary is never complete rendering name translation from English to Chinese ineffective. One way to solve this problem is not to rely on a dictionary alone but to adopt automatic translation according to pronunciation similarities, i.e. to map phonemes comprising an Engli...

Transliterations play an important role in multilingual entity reference resolution, because proper names increasingly travel between languages in news and social media. Previous work associated with machine translation targets transliteration only single between language pairs, focuses on specific classes of entities (such as cities and celebrities) and relies on manual curation, which limits ...

In this paper, we present a novel backward transliteration approach which can further assist the existing statistical model by mining monolingual web resources. Firstly, we employ the syllable-based search to revise the transliteration candidates from the statistical model. By mapping all of them into existing words, we can filter or correct some pseudo candidates and improve the overall recall...