A comparative study of stemming algorithms for use with the Uzbek language

Ismailov, A.; Jalil, Masita Masila Abdul; Abdullah, Zailani; Rahim, N. H. Abd

doi:10.1109/iccoins.2016.7783180

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…Algoritma-algoritma tersebut umum digunakan untuk menghapus awalan dan akhiran dalam bahasa Inggris. Konsep penghapusan menggunakan aturan lexicon dan rule-based [11]. Selain itu algoritma khusus berdasar aturan bahasa tertentu juga pernah dikembangkan antara lain untuk bahasa Arab [12], Hindi [13], Uzbek [11], dan Indonesia [6] [14].…”

Section: Pendahuluanunclassified

“…Konsep penghapusan menggunakan aturan lexicon dan rule-based [11]. Selain itu algoritma khusus berdasar aturan bahasa tertentu juga pernah dikembangkan antara lain untuk bahasa Arab [12], Hindi [13], Uzbek [11], dan Indonesia [6] [14]. Stemming bahasa Jawa dapat dilakukan dengan memodifikasi aturan Stemming bahasa Indonesia.…”

Section: Pendahuluanunclassified

“…Hal ini diharapkan dapat meningkatkan akurasi Stemming berbasis DLD. [29] dan Uzbek [11] yang tidak memiliki sisipan.…”

Section: Metodologiunclassified

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Stemming Bahasa Jawa Menggunakan Damerau Levenshtein Distance (Dld)

Wibawa

Hakim

2021

j. Teknik inform.

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Stemming is one of the essential stages of text mining. This process removes prefixes and suffixes to produce root words in a text. This study uses a string matching algorithm, namely Damerau Levenshtein Distance (DLD), to find the basic word forms of Javanese. Test data of 300 words that have a prefix, insertion, suffix, a combination of prefix and suffix, and word repetition. The results of this study indicate that the Damerau Levenshtein Distance (DLD) algorithm can be used for Stemming Javanese text with an accuracy value of 49.6%.

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Section: Pendahuluanunclassified

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Stemming Bahasa Jawa Menggunakan Damerau Levenshtein Distance (Dld)

Wibawa

Hakim

2021

j. Teknik inform.

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“…Porter stemmer, Lovins, Dawson [35] are some of the popular stemming algorithm for English language. Since for Gujarati language no stemmer is available to use, we have developed our own stemmer.…”

Section: Figure 2 Proposed Text Query-based Video Retrieval Approachmentioning

confidence: 99%

News Story Retrieval Based on Textual Query

Dave¹,

Holia²

2020

IJEAT

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This paper presents news video retrieval using text query for Gujarati language news videos. Due to the fact that Broadcasted Video in India is lacking in metadata information such as closed captioning, transcriptions etc., retrieval of videos based on text data is trivial task for most of the Indian language video. To retrieve specific story based on text query in regional language is the key idea behind our approach. Broadcast video is segmented to get shots representing small news stories. To represent each shot efficiently, key frame extraction using singular value decomposition and rank of matrix is proposed. Text is extracted from keyframes for further indexing data. Next task is to process text using natural language processing steps like tokenization, punctuation and extra symbols removal as well as stemming of words to root words etc. Due to unavailability of stemming and other methods of preprocessing of text in Guajarati language, we have given basic stemming technique to reduce dictionary size for efficient indexing of text data. With proposed system 82.5 percent accuracy is achieved on Gujarati news video dataset ETV.

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“…For example, in a meeting about the parking problem on campus, key words could include several nouns such as "bus," "campus," "car," and "bicycle" as well as several verbs and adjectives such as "abolish," "allowed," "commute," "difficult," and "easier." In addition, the size of the keyword list can be further reduced by "stemming" the content words [14], i.e., removing the prefix, suffix, or both so that only the root of the word remains.…”

Section: Identifying Irrelevant Textmentioning

confidence: 99%

Automatic Detection of Irrelevant Comments in an Electronic Meeting

Aiken

Garner

2017

IJMIT

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I S S N 2 2 7 8 -5 6 1 2 V o l u m e 12 N u m b e r 1 I n t e r n a t i o n a l J o u r n a l o f M a n a g e m e n t a n d I n f o r m a t i o n T e c h n o l o g y 3123 | P a g e M ay 2 0 1 7 w w w . c i r w o r l d . c o m Automatic Detection of Irrelevant Comments in an Electronic MeetingMilam Aiken, Bart GarnerSchool of Business Administration, University of Mississippi, University, MS 38677 ABSTRACTGroups exchanging ideas in electronic meetings often generate irrelevant or off-topic comments that can detract from the conversation. Here, we describe a system that seeks to identify this immaterial text using previously identified keywords.Results of an experiment with the system show that group members believe meetings do have irrelevant comments that waste time, but participants often enjoy them. The system achieved an F measure of 42.3% for recall and precision, and further research is necessary to determine if this is sufficient or what can be done to improve this score.

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A comparative study of stemming algorithms for use with the Uzbek language

Cited by 10 publications

References 18 publications

Stemming Bahasa Jawa Menggunakan Damerau Levenshtein Distance (Dld)

Stemming Bahasa Jawa Menggunakan Damerau Levenshtein Distance (Dld)

News Story Retrieval Based on Textual Query

Automatic Detection of Irrelevant Comments in an Electronic Meeting

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