No abstract

Language transliteration is one of the important areas in NLP. Transliteration is very useful for converting the named entities (NEs) written in one script to another script in NLP applications like Cross Lingual Information Retrieval (CLIR), Multilingual Voice Chat Applications and Real Time Machine Translation (MT). The most important requirement of Transliteration system is to preserve the phonetic properties of source language after the transliteration in target language. In this paper, we have proposed the named entity transliteration for Hindi to English and Marathi to English language pairs using Support Vector Machine (SVM). In the proposed approach, the source named entity is segmented into transliteration units; hence transliteration problem can be viewed as sequence labeling problem. The classification of phonetic units is done by using the polynomial kernel function of Support Vector Machine (SVM). Proposed approach uses phonetic of the source language and n-gram as two features for transliteration.

No abstract

e-Governance and Web based online commercial multilingual applications has given utmost importance to the task of translation and transliteration. The Named Entities and Technical Terms occur in the source language of translation are called out of vocabulary words as they are not available in the multilingual corpus or dictionary used to support translation process. These Named Entities and Technical Terms need to be transliterated from source language to target language without losing their phonetic properties. The fundamental problem in India is that there is no set of rules available to write the spellings in English for Indian languages according to the linguistics. People are writing different spellings for the same name at different places. This fact certainly affects the Top-1 accuracy of the transliteration and in turn the translation process. Major issue noticed by us is the transliteration of named entities consisting three syllables or three phonetic units in Hindi and Marathi languages where people use mixed approach to write the spelling either by orthographical approach or by phonological approach. In this paper authors have provided their opinion through experimentation about appropriateness of either approach.

Scour is a complex phenomenon, its complexity increases with the change in the geometry of the obstruction. Most of the investigations were carried out on the scour process at a uniform pier. However, in reality, many bridge piers behave as non-uniform depending on the exposure of their foundation into the flow field. All the experimental investigations were carried out in the present study to understand the effect of pier geometry, the position of footing top with respect to bed level, and sediment mixtures (uniform and non-uniform) on local scour under clear water condition. A total of 106 experiments were conducted in the present study with a different combination of pier models, sediment mixture, and footing top with respect to bed. A maximum scour depth model was developed using 182 data points consisting of experimental data (106) and extracted from the literature (76). To develop a model, state-of-the-art Artificial Intelligence (AI) based modeling technique known as Gene Expression Programming (GEP) was employed in this study. GEP model was developed by using 130 data points and independent 52 data points for the model validation. The performance of the proposed scour model for the compound bridge pier was found to be satisfactory.