Sanskrit Stopword Analysis Through Morphological Analyzer and Its Gujarati Equivalent for MT System

Raulji, Jaideepsinh K.; Saini, Jatinderkumar R.

doi:10.1007/978-981-15-0630-7_42

Cited by 5 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fayaza and Farhath [36] presented a stop word list for Tamil language. Similarly, Kaur and Saini [22,23] worked for the stop-word list of Punjabi language, Rakholia and Saini [24,25] worked for the stop-word list of Gujarati language while Raulji and Saini [31,32] worked for the stop-word list of Sanskrit language. A stop word list based on the Rainbow statistical text has also been presented by Shuson [38].…”

Section: Literature Reviewmentioning

confidence: 99%

DBTechVoc: A POS-tagged Vocabulary of Tokens and Lemmata of the Database Technical Domain

Saini¹,

Kotecha²,

Gaikwad³

2022

IJACSA

View full text Add to dashboard Cite

Vocabulary of a language has a great role to play in the Natural Language Processing (NLP) applications. Such applications make use of lists like stop-word list, general service list, academic word list and technical domain word list. The technical domain word list differs with each domain and though it is available for fields like medicine, biology, computer science, physics and law, the domain of databases in specific has still not been explored. For the first time, we propose technical vocabulary comprising of POS-tagged unigram tokens and POStagged unigram lemmata for the technical domain of databases. This vocabulary has been called DBTechVoc with a coined term. Notably, the multi-word phrases have also been considered, without their further tokenization, to maintain their semantics. The empirical results, with more than 1000 high quality research papers collected over a period of 45 years from 1976 to 2021, prove that the technical general word list of the domain of computer science is different from the technical and specific word list of the domain of databases. The overlap was found to be less than 2%. The research titles use 6% Rainbow stop words while 13% of the words used for the research paper titles are inflectional forms of lemmata.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

DBTechVoc: A POS-tagged Vocabulary of Tokens and Lemmata of the Database Technical Domain

Saini¹,

Kotecha²,

Gaikwad³

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…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]. Hindi text analysis of poetry was presented through an automated system for generation of metadata [40].…”

Section: Literature Reviewmentioning

confidence: 99%

Marathi Text Analysis using Unsupervised Learning and Word Cloud

Bafna¹,

Saini²

2020

IJEAT

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Sanskrit Stopword Analysis Through Morphological Analyzer and Its Gujarati Equivalent for MT System

Cited by 5 publications

References 9 publications

DBTechVoc: A POS-tagged Vocabulary of Tokens and Lemmata of the Database Technical Domain

DBTechVoc: A POS-tagged Vocabulary of Tokens and Lemmata of the Database Technical Domain

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

Contact Info

Product

Resources

About