Indian Languages IR using Latent Semantic Indexing

Sivakumar, A.; Premchand, P.; Govardhan, A.

doi:10.5121/ijcsit.2011.3419

Cited by 7 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sivakumar et al [7] developed a Hindi-English CLIR system that identifies equivalent English document for the given Hindi document based on cosine similarity measure. The features of the documents to find the similarity are reduced using latent semantic indexing.…”

Section: Clirmentioning

confidence: 99%

“…It is difficult for the users *For correspondence such as farmers to pose such queries. CLIR systems [6][7][8][9][10] facilitate non-English users to pose natural language queries in their own languages but fail to handle semantics. A few research works [11][12][13][14][15] have been reported on ontology-based CLIR systems that deal with semantics using bilingual ontologies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ontology-based Tamil–English cross-lingual information retrieval system

Thenmozhi¹,

Aravindan²

2018

Sādhanā

View full text Add to dashboard Cite

Cross-lingual information retrieval (CLIR) systems facilitate users to query for information in one language and retrieve relevant documents in another language. In general, CLIR systems translate query in source language to target language and retrieve documents in target language based on the keywords present in the translated query. However, the presence of ambiguity in source and translated queries reduces the performance of the system. Ontology can be used to address this problem. The current approaches to ontology-based CLIR systems use manually constructed multilingual ontology, which is expensive. However, many methods exist to automatically construct ontology for any domain in English but not in other languages like Tamil. We propose a methodology for Tamil-English CLIR system by translating the Tamil query to English and retrieve pages in English to address these issues. Our approach uses a word sense disambiguation module to resolve the ambiguity in Tamil query. An automatically constructed ontology in English is used to address the ambiguity of English query. We have developed a morphological analyser for Tamil language, Tamil-English bilingual dictionary and named entity database to translate a Tamil query to English. The translated query is reformulated using ontology and the reformulated queries are given to a search engine to retrieve English documents from the Internet. We have evaluated our methodology for agriculture domain and the evaluation results show that our approach outperforms other approaches in terms of precision.

show abstract

Section: Clirmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ontology-based Tamil–English cross-lingual information retrieval system

Thenmozhi¹,

Aravindan²

2018

Sādhanā

View full text Add to dashboard Cite

show abstract

“…A concept based retrieval is known as Latent Semantic Indexing (LSI), is a method that demonstrates the singular value decomposition (SVD) [4] and vector space ranking. A.P.SivaKumar.et.al [5] proposes a combination of Latent Semantic Indexing technique with SVD to achieve efficient retrieval for Hindi and English. The limitation of the system is expensive and works only in small document collection.…”

Section: Introductionmentioning

confidence: 99%

An information retrieval system for Malayalam using query expansion technique

Babu¹,

Sindhu²

2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

View full text Add to dashboard Cite

Malayalam is the administrative language of the south Indian state of Kerala and also of the Lakshadweep islands of west coast of India. This work explain an efficient monolingual information retrieval system for Malayalam using query expansion technique, which returns Malayalam documents in response to query in Malayalam. The proposed system uses synonym mapping to improve the efficiency of document retrieval. This technique help to overcome vocabulary mismatch issues by expanding user query with additional relevant terms and reweighting the terms in expanded user query. The developed system is domain independent and used in different Natural Language Processing (NLP) tasks in Classical language like Malayalam.

show abstract

“…A.P.SivaKumar.et.al [11] uses LSI technique with Singular Value Decomposition (SVD) to achieve effective indexing for English and Hindi languages. This paper uses the Singular Value Decomposition (SVD) of LSI technique and it solves the problem of polysemy and synonymy.…”

Section: Related Workmentioning

confidence: 99%

“…i)Assumes independence of index terms Latent Semantic Indexing i)A.P.SivaKu mar.et.al [11] ii) Neelam Phadnis and Jayant Gadge [9]s i) Retrieving text based on concept.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Survey of Information Retrieval Models for Malayalam Language Processing

Babu¹,

Sindhu²

2014

IJCA

View full text Add to dashboard Cite

Information retrieval is an important research area in computer science. Information retrieval is concerned with the storage of text documents and their subsequent retrieval in response to user's requests for information. There are several Information Retrieval models developed for efficient document retrieval. In this survey paper we describe major IR models which are used for various document retrieval purposes. A suitable method for an effective Malayalam monolingual information retrieval system is also proposed here. General Terms

show abstract

Indian Languages IR using Latent Semantic Indexing

Cited by 7 publications

References 4 publications

Ontology-based Tamil–English cross-lingual information retrieval system

Ontology-based Tamil–English cross-lingual information retrieval system

An information retrieval system for Malayalam using query expansion technique

A Survey of Information Retrieval Models for Malayalam Language Processing

Contact Info

Product

Resources

About