Evaluation of Dynamic Bayesian Network models for Entity Name Transliteration

Mining Transliterations from Wikipedia using Dynamic Bayesian Networks

Transliteration mining is aimed at building high quality multi-lingual named entity (NE) lexicons for improving performance in various Natural Language Processing (NLP) tasks including Machine Translation (MT) and Cross Language Information Retrieval (CLIR). In this paper, we apply two Dynamic Bayesian network (DBN)-based edit distance (ED) approaches in mining transliteration pairs from Wikipe...

Dynamic Bayesian Networks for Transliteration Discovery and Generation

This project is involved with extraction and transliteration of entity names between languages that use different writing systems (or alphabets). Extraction involves the automatic identification of sequences in parallel/comparable corpora/text that can be considered as proper entity names. On the other hand, transliteration generation involves automatic transformation of a source language name ...

Clustering and Classifying Person Names by Origin

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...

Applying a Dynamic Bayesian Network Framework to Transliteration Identification

Identification of transliterations is aimed at enriching multilingual lexicons and improving performance in various Natural Language Processing (NLP) applications including Cross Language Information Retrieval (CLIR) and Machine Translation (MT). This paper describes work aimed at using the widely applied graphical models approach of ‘Dynamic Bayesian Networks (DBNs) to transliteration identifi...

Applying Dynamic Bayesian Networks in Transliteration Identification