Corpus ID: 14322565

An Approach for Solving of Natural Language Queries and Transliteration using Multi-Agent System

@inproceedings{Shelke2010AnAF,
  title={An Approach for Solving of Natural Language Queries and Transliteration using Multi-Agent System},
  author={N. Shelke and R. Dharaskar},
  year={2010}
}
Since the invention of computer researcher fraternity is trying to minimize the communication gap between the computer and a human. Since then continuous and consistent efforts are being made to develop Natural Language Interfaces to Databases (NLIDBs). A general user is not an expert in SQL, but knows general English or native language for computer interaction. A majority of school-going children pursue their education in regional languages, among which Hindi language stands out to be most… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 24 REFERENCES
Named Entity Transliteration
TLDR
An improved modified joint source-channel model that is used to transliterate the Bengali Named Entity into English and vice versa performs the best during Bengali to English transliteration with a Word Agreement Ratio of 81.4% and a Transliteration Unit Agreement ratio of 95.7%. Expand
A Modified Joint Source-Channel Model for Transliteration
TLDR
A framework has been presented that allows direct orthographical mapping between two languages that are of different origins employing different alphabet sets and a Bengali-English machine transliteration system has been developed based on the proposed models. Expand
Combining bidirectional translation and synonymy for cross-language information retrieval
This paper introduces a general framework for the use of translation probabilities in cross-language information retrieval based on the notion that information retrieval fundamentally requiresExpand
Transliteration of Proper Names in Cross-Lingual Information Retrieval
TLDR
The application of statistical machine translation techniques to "translate" the phonemic representation of an English name to a sequence of initials and finals, commonly used sub-word units of pronunciation for Chinese in support of cross-lingual speech and text processing applications. Expand
A Comparative Study of Named Entity Recognition for Hindi Using Sequential Learning Algorithms
Through this paper we present a comparative study of two sequential learning algorithms viz. Conditional Random Fields (CRF) and Static Vector Machine (SVM) applied to the task of Named EntityExpand
Artificial Intelligence: A Modern Approach
The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.Expand
Character N-Grams Translation in Cross-Language Information Retrieval
This paper describes a new technique for the direct translation of character n-grams for use in Cross-Language Information Retrieval systems. This solution avoids the need for word normalizationExpand
Transliteration considering context information based on the maximum entropy method
TLDR
The proposed method successfully transliterates an English word not registered in any bilingual or pronunciation dictionaries by converting each partial letters in the English word into Japanese katakana characters. Expand
Hindi-english cross-lingual question-answering system
We developed a cross-lingual, question-answering (CLQA) system for Hindi and English. It accepts questions in English, finds candidate answers in Hindi newspapers, and translates the answerExpand
Translating Named Entities Using Monolingual and Bilingual Resources
TLDR
A novel algorithm for translating named entity phrases using easily obtainable monolingual and bilingual resources is presented and evaluation of this algorithm in translating Arabic named entities to English is reported on. Expand
...
1
2
3
...