Hindi to English Machine Transliteration of Named Entities using Conditional Random Fields
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TLDR
This paper focuses on Hindi to English machine transliteration of Indian named entities such as proper nouns, place names and organization names using conditional random fields (CRF).Abstract:
Machine transliteration has received significant research attention in recent years. In most cases, the source language has been English and the target language is an Asian language. This paper focuses on Hindi to English machine transliteration of Indian named entities such as proper nouns, place names and organization names using conditional random fields (CRF). Hindi is the national language of the India and spoken by more than 500 millions Indian. Hindi is the world‟s fourth most commonly used language after Chinese, English and Spanish. This system takes Indian place name as an input in Hindi language using Devanagari script and transliterates it into English. The input to the system is provided in the form of syllabification in order to apply the n-gram techniques. As more than 50% named entities are formed as a combination of two and three syllabic units, the ngram approach with unigrams, bigrams and trigrams of Hindi are used to train the corpus. The system provides the satisfactory performance for trigrams as compared to unigrams and bigrams. General Terms Machine Transliterationread more
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References
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Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
An Introduction to Conditional Random Fields for Relational Learning
Charles Sutton,Andrew McCallum +1 more
TL;DR: A solution to this problem is to directly model the conditional distribution p(y|x), which is sufficient for classification, and this is the approach taken by conditional random fields.
Journal Article
Machine transliteration
Kevin Knight,Jonathan Graehl +1 more
TL;DR: This paper used a generative model to perform backwards transliteration from Japanese back to English, and evaluated a method for performing backward transliterations by machine, incorporating several distinct stages in the translation process.