Rule based stemmer in Urdu

Gupta, Vaishali; Joshi, Nisheeth; Mathur, Iti

doi:10.1109/iccct.2013.6749615

Cited by 22 publications

(13 citation statements)

References 7 publications

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“…Stemming helps in improving the IR performance especially in terms of recall (Pandey and Siddiqui 2009). Gupta et al (2013) shown the effectiveness of rule-based Urdu stemmer for IR task. 119 rules are made for 2000 words dataset and 86.5 % accuracy is achieved.…”

Section: Information Retrieval (Ir)mentioning

confidence: 98%

See 1 more Smart Citation

Urdu language processing: a survey

2016

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Extensive work has been done on different activities of natural language processing for Western languages as compared to its Eastern counterparts particularly South Asian Languages. Western languages are termed as resource-rich languages. Core linguistic resources e.g. corpora, WordNet, dictionaries, gazetteers and associated tools being developed for Western languages are customarily available. Most South Asian Languages are low resource languages e.g. Urdu is a South Asian Language, which is among the widely spoken languages of sub-continent. Due to resources scarcity not enough work has been conducted for Urdu. The core objective of this paper is to present a survey regarding different linguistic resources that exist for Urdu language processing, to highlight different tasks in Urdu language processing and to discuss different state of the art available techniques. Conclusively, this paper attempts to describe in detail the recent increase in interest and progress made in Urdu language processing research. Initially, the available datasets for Urdu language are discussed. Characteristic, resource sharing between Hindi and Urdu, orthography, and morphology of Urdu language are provided. The aspects of the pre-processing activities such as stop words removal, Diacritics removal, Normalization and Stemming are illustrated. A review of state of the art research for the tasks such as Tokenization, Sentence Boundary Detection, Part of Speech tagging, Named Entity Recognition, Parsing and development of WordNet tasks are discussed. In addition, impact of ULP on application areas, such as, Information Retrieval, Classification and plagiarism detection is investigated. Finally, open issues and future directions for this new and dynamic area of research are provided. The goal of this paper is to organize the ULP work in a way that it can provide a platform for ULP research activities in future.

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Section: Information Retrieval (Ir)mentioning

confidence: 98%

“…Data in these corpora are organized in the form of verbs, nouns, adjectives, punctuations, numbers, special symbols etc. Gupta et al (2013) developed a rule base Urdu stemmer and evaluated its performance on IR task. They tested their proposed Urdu stemmer on 2000 words.…”

Section: Stemmingmentioning

confidence: 99%

Urdu language processing: a survey

2016

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“…He established and implemented rules for this purpose to eliminate the suffix and prefix from the inflected words of Urdu. In addition the rule based stemmer for the Urdu language was created by Gupta [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Study of Truncating and Statistical Stemming Algorithms

Memon¹,

Mallah²,

K.N.Memon³

et al. 2020

IJACSA

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Search and indexing systems bear a significant quality called word stemming, is lump of content excavating requests, IR frameworks and natural language handling frameworks. The fundamental topic in the search and indexing through time is to upgrade infer via robotized diminishing and fussing of the words into word roots. From index term by evacuating any connected prefixes and postfixes, Stemming is done to proceeding piece of work of index word, and more extensive idea than the real word is spoken by trunk. In an IR framework, the numeral of recovered archives is expanded by stemming process.

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“…Joshi et al [19] further developed a technique to using machine learning in evaluating MT engines. Tyagi et al [20] [21] developed an approach of translating complex English sentences by first simplifying them and then translating into Hindi. Yogi et al [22] developed an approach to identify candidate translation which are good for post editing.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Part of Speech Tagger Using Deep Learning

Singh*¹,

Verma²,

Seal³

et al. 2019

IJEAT

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Part of speech tagging is the initial step in development of NLP (natural language processing) application. POS Tagging is sequence labelling task in which we assign Part-of-speech to every word (Wi) which is sequence in sentence and tag (Ti) to corresponding word as label such as (Wi/Ti…. Wn/Tn). In this research project part of speech tagging is perform on Hindi. Hindi is the fourth most popular language and spoken by approximately 4billion people across the globe. Hindi is free word-order language and morphologically rich language due to this applying Part of Speech tagging is very challenging task. In this paper we have shown the development of POS tagging using neural approach.

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Rule based stemmer in Urdu

Cited by 22 publications

References 7 publications

Urdu language processing: a survey

Urdu language processing: a survey

Comparative Study of Truncating and Statistical Stemming Algorithms

Development of Part of Speech Tagger Using Deep Learning

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