Information Retrieval for Gujarati Language Using Cosine Similarity Based Vector Space Model

Rakholia, Rajnish M.; Saini, Jatinderkumar R.

doi:10.1007/978-981-10-3156-4_1

Cited by 20 publications

(11 citation statements)

References 4 publications

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“…They applied an automatic and dynamic approach to identify stop words from Gujarati documents and claimed 94.08% average accuracy. www.ijacsa.thesai.org Research works involving Natural Language Processing (NLP) of Gujarati language have been presented for MTS for Sanskrit-Gujarati pair [18], comparison of morphologically analyzed words [19], bilingual dictionary implementation [20], constituency mapper [21], classification [22] and information retrieval [23] to name a few.…”

Section: Related Literature Reviewmentioning

confidence: 99%

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Modh¹,

Saini²

2021

IJACSA

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Gujarati is the language used for everyday communication in the state of Gujarat, India. The Gujarati language is also officially recognized by the constitution and the government of India. Gujarati script is based on the Devanagari script. An idiom is an expression, phrase, or word that has a different meaning from the literal meaning of the words in it. Idioms represent the cultural heritage of Gujarati language. Idioms are used in Gujarati language for effective communication and convey of an accurate message. No Machine Translation System does the accurate translation of Gujarati idioms to English or any other language. Different idiom phrases can be generated by adding diacritic(s) as well as suffix to the root or base form of the idiom. Many forms of single idiom make automatic idiom identification as well as machine translation more challenging. This paper focuses on the design and implementation of diacritics and suffix-based rules for dynamic phrase generation and detection of idioms of Gujarati language. This implementation helps in identifying Gujarati idiom present in any possible form in the Gujarati text. The obtained results with the execution of 7050 different Gujarati idiom phrases yield an accuracy of 99.73%. The results are encouraging enough to make the proposed implementation useful for Natural Language processing tasks related to Gujarati language idioms.

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Section: Related Literature Reviewmentioning

confidence: 99%

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Modh¹,

Saini²

2021

IJACSA

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“…For text analysis of Indian languages, Punjabi has been explored through stop words identification [22] and categorization [23] as well as poetry corpus creation [24] and classification [25][26]. Gujarati has been, similarly explored through diacritic extraction technique [27], information retrieval [28], stop words identification [29] and categorization [30], Machine Translation System (MTS) [31][32] and classification [33]. Sanskrit has been explored through stop word generation [34] and analysis [35], bilingual dictionary [36], constituency mapper [37], lemmatizer development [38] and comparison of its morphological analyzers [39].…”

Section: Literature Reviewmentioning

confidence: 99%

Marathi Text Analysis using Unsupervised Learning and Word Cloud

Bafna¹,

Saini²

2020

IJEAT

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Managing a large number of textual documents is a critical and significant task and supports many applications ranging from information retrieval to clustering search engine results. Marathi is one of the oldest of the regional languages in the Indo-Aryan language family, dating from about AD 1000. Abundance of Marathi literature has generated a big corpus and need of summarization of information. The objective of this study is to overcome the scalability problem while managing the documents and summarize the Marathi corpus by extracting tokens. The work is better in terms of scalability and supports the consistent quality of cluster for incremental data set. Most of the past and contemporary research works have targeted English corpus document management. Marathi corpus has been mostly exploited by the researchers for exploring stemming, single-document summarization and classifier design on Marathi corpus. Implementing unsupervised learning on the Marathi corpus for summarization of multiple documents through Word Cloud is still an untouched area. Technically speaking, the current work is an application of TF-IDF, cosine-based document similarity measures and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities. Entropy and precision are used to evaluate the experiments carried on different datasets and results prove the robustness of the proposed approach for Marathi Corpus.

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“…Rakholia and Saini [8] have presented a rule based approach to dynamically identify stop words for Gujarati language. Vandana Jha et al [9] developed an algorithm to remove stopwords from the Hindi text based on Deterministic finite automata. The algorithm also tested on 200 documents and succeeded 99% accuracy and time efficiency.…”

Section: Related Workmentioning

confidence: 99%

Automatic Generation of Stopwords in the Amharic Text

Miretie¹,

Khedkar²

2018

IJCA

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For the retrieval of information from documents of different natural languages, pre-processing of the document is the main task. During pre-processing, words which occur too frequently and have little semantic in the document should be identified. Such words are called Stopwords. Stopwords list for different world languages like English, Chinese, Hindi, Arabic Sanskrit etc. are identified. But as I long as I know there is no standard method to identify these words for the Amharic language. In this paper, we proposed the automatic identification of Stopwords for the Amharic text by an aggregate based methodology of words frequency, inverse document frequency, and entropy value measure. Available works on Stopwords identification techniques are based on static or dictionary based Stopwords lists. This method inefficient and very expensive and it is a time-consuming task as the searching process takes a long time. The proposed work will overcome these problems using aggregated methods of both frequency measures and entropy measures of words in the Amharic text for the automatic Stopwords identification.

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Information Retrieval for Gujarati Language Using Cosine Similarity Based Vector Space Model

Cited by 20 publications

References 4 publications

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Marathi Text Analysis using Unsupervised Learning and Word Cloud

Automatic Generation of Stopwords in the Amharic Text

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