In natural language processing, information about a person’s geographical origin is an important feature for name entity transliteration and question answering. We propose a language-independent name origin clustering and classification framework. Provided with a small amount of bilingual name translation pairs with labeled origins, we measure origin similarities based on the perplexities of na...

Machine transliteration is an automatic method to generate characters or words in one alphabetical system for the corresponding characters in another alphabetical system. There has been increasing concern on machine transliteration as an assistant of machine translation and information retrieval. Three machine transliteration models, including “grapheme-based model”, “phonemebased model”, and “...

Transliteration is the process of writing a word (mainly proper noun) from one language in the alphabet of another language. This process requires mapping the pronunciation of the word from the source language to the closest possible pronunciation in the target language. In this paper we introduce a new semi-supervised transliteration mining method for parallel and comparable corpora. The metho...

Existing named entity (NE) transliteration approaches often exploit a general model to transliterate NEs, regardless of their origins. As a result, both a Chinese name and a French name (assuming it is already translated into Chinese) will be translated into English using the same model, which often leads to unsatisfactory performance. In this paper we propose a cluster-specific NE transliterat...

Transliteration has been one of the common methods for multilingual text input. Many earlier methods employed transliteration schemes for defining one to one mapping of input character combinations to output character combinations. Though such well defined mappings made it easier to write a transliteration program, the end user was burdened with learning the mappings. Further, though transliter...

DEVELOPMENT OF A PUNJABI TO ENGLISH TRANSLITERATION SYSTEM Kamal Deep1 and Vishal Goyal2 1Department of Computer Science, Punjabi University, Patiala, India E-mail: [email protected] 2Assistant Professor, Department of Computer Science, Punjabi University, Patiala, India E-mail: [email protected] Machine transliteration has gained prime importance as a supporting tool for Machine translat...

At the heart of SwiftKey's success are well motivated Machine Learning and Natural Language Processing principles. But that foundation is only the start, it's also required relentless focus on User Experience, solving endless real world issues and building and connecting with the vibrant community of SwiftKey users worldwide. This talk will take you through the story of how we turned a great IM...

Machine transliteration is an important Natural Language Processing task. This paper proposes a Noisy Channel Model for Grapheme-based machine transliteration. Moses, a phrase-based Statistical Machine Translation tool, is employed for the implementation of the system. Experiments are carried out on the NEWS 2009 Machine Transliteration Shared Task English-Chinese track. EnglishChinese back tra...

Existing named entity (NE) transliteration approaches often exploit a general model to transliterate NEs, regardless of their origins. As a result, both a Chinese name and a French name (assuming it is already translated into Chinese) will be translated into English using the same model, which often leads to unsatisfactory performance. In this paper we propose a cluster-specific NE transliterat...

In this paper, we present methods of transliteration and back-transliteration. In Korean technical documents and web documents, many English words and Japanese words are transliterated into Korean words. These transliterated words are usually technical terms and proper nouns, so it is hard to find them in a dictionary. Therefore an automatic transliteration system is needed. Previous transliter...