Language model-based retrieval for Farsi documents

Taghva, Kazem; Coombs, Jeffrey; Pareda, R.; Nartker, Thomas A.

doi:10.1109/itcc.2004.1286582

Cited by 15 publications

(14 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In most cases, morphological variants of words have similar semantic interpretations and can be considered as equivalent for the purpose of IR applications [22]. Persian has a complex morphology.…”

Section: B Persian Lemmatizationmentioning

confidence: 99%

“…Thus, the key terms of a query or document are represented by stems rather than by the original words. For this reason, we uses the lemmatizer proposed in [8], [22].…”

Section: B Persian Lemmatizationmentioning

confidence: 99%

“…This means that total number of words can be decreased into a few words that considered as stem, and the rest of the words are derivation this stems. Deriving a word of the principal stem causes that the main concept of that word takes a better shape, or expresses a syntax role in a sentence [22]. With this rule we stemmed both noun and verb to its root completely, Checking nouns and verbs against of their special Affixes and extracting stem words from nouns and verbs separately.…”

Section: A Text Preprocessingmentioning

confidence: 99%

See 2 more Smart Citations

A Distributed N-Gram Indexing System to Optimizing Persian Information Retrieval

Danesh¹,

Minaei²,

Kashefi³

2013

IJCTE

View full text Add to dashboard Cite

Abstract-As the amount of information and the number of queries has been increasing today, indexing is a good solution to fight with the inherent complexity of text retrieval and accelerating information retrieval in different languages. Also N-Gram Indexing is a solution of the issues such as stemming, misspellings, multilingual and partial matching and has the advantages of language independent and error endurance. Persian is a name of a language which is common in the Middle East. It is spoken in some countries like Iran, Afghanistan and Tajikistan. Therefore, Persian is the language of many documents is published on the net. But, not more researches have been done about the Persian documents retrieval. In this paper, we present a method for Persian documents retrieving using N-gram indexing and distribution technique. The proposed index is a method of more effective answering queries that increases the quality of information retrieval substantially and we gain more optimizing retrieval in Persian documents. But the speed of N-gram indexing is low; to solve this problem we design a distributed N-gram indexing mechanism for large systems of Persian language. Compare with the other methods in this field, we improve the quality of retrieved documents and also the speed of information retrieval.

show abstract

Section: B Persian Lemmatizationmentioning

confidence: 99%

“…Thus, the key terms of a query or document are represented by stems rather than by the original words. For this reason, we uses the lemmatizer proposed in [8], [22].…”

Section: B Persian Lemmatizationmentioning

confidence: 99%

Section: A Text Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

A Distributed N-Gram Indexing System to Optimizing Persian Information Retrieval

Danesh¹,

Minaei²,

Kashefi³

2013

IJCTE

View full text Add to dashboard Cite

show abstract

“…Their comparison shows that the performance of FuFaIR is considerably better than that of vector space model. Also experiments in [4] suggest the usefulness of language modeling techniques for Farsi. Furthermore, the design and implementation of a Farsi stemmer is reported in [5].…”

Section: Related Workmentioning

confidence: 99%

N-gram and Local Context Analysis for Persian text retrieval

AleAhmad

Hakimian

Mahdikhani

et al. 2007

2007 9th International Symposium on Signal Processing and Its Applications

View full text Add to dashboard Cite

show abstract

“…In [1] the author has described more than 200 combinations of retrieval models, term weights and methods that have been tested on Persian text and their results. Experiments in [8] suggested the usefulness of language modeling techniques for Farsi. Furthermore, the design and implementation of a Farsi stemmer is reported in [8].…”

Section: Related Workmentioning

confidence: 99%

FuFaIR: a Fuzzy Farsi Information Retrieval System

Nayyeri

Oroumchian

2006

IEEE International Conference on Computer Systems and Applications, 2006.

View full text Add to dashboard Cite

Persian (Farsi) is one of the languages of Middle East. There are significant amount of Persian documents available in digital form and even more are created every day. Therefore, there is a necessity to implement Information Retrieval System with high precision for this language. This paper discusses the design, implementation and testing of a Fuzzy retrieval system for Persian called FuFaIR. This system also supports Fuzzy quantifiers in its query language. Tests have been conducted using a standard Persian test corpus called Hamshari. The performance results obtained from FuFaIR are positive and they indicate that the FuFaIR could notably outperform well known industry systems such as the vector space model.

show abstract

Language model-based retrieval for Farsi documents

Abstract: This paper reports on an application of LanguageModeling techniques to the retrieval of Farsi documents. We discovered that Language Modeling improves the precision of retrieval when compared to a standard vector space model.

Cited by 15 publications

References 7 publications

A Distributed N-Gram Indexing System to Optimizing Persian Information Retrieval

A Distributed N-Gram Indexing System to Optimizing Persian Information Retrieval

N-gram and Local Context Analysis for Persian text retrieval

FuFaIR: a Fuzzy Farsi Information Retrieval System

Contact Info

Product

Resources

About