Towards Full Lexical Recognition

Pavlović-Lažetić, Gordana; Vitas, Duško; Krstev, Cvetana

doi:10.1007/978-3-540-30120-2_23

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The DELAS-TOP and DELAS-PERS are dictionaries that respectively list geographic names and Serbian personal names (Krstev et al, 2008;Pavlović-Lažetić et al, 2004;Grass et al, 2002). The dictionary of geographic names DELA-TOP covers geographic concepts at the level of a high-school atlas (approximately 20.000 toponyms, oronyms, and hydronyms with their corresponding derivatives).…”

Section: Dictionaries and Terminologiesmentioning

confidence: 99%

Creating a stop word dictionary in Serbian

Marovac¹,

Avdić²,

Ljajić³

2021

Sci Pub Univ Novi Pazar Ser A

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By using natural language processing techniques, it is possible to get a lot of information from the extraction of document topics through mapping of document key words or content-based classification of documents, etc. To get this information, an important step is to separate words that carries informative value in a sentence from those words that do not affect its meaning. By using dictionaries of stop words specific to each natural language, the marking of words that do not carry meaning in the sentence is achieved. This paper presents creating a stop word dictionary in Serbian. The influence of stop words to the text processing is presented on three different data set. It is shown that by using proposed dictionary of Serbian stop words the data set dimension is reduced from 15% to 39%, while the quality of the obtained n-gram language models is improved.

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Section: Dictionaries and Terminologiesmentioning

confidence: 99%

Creating a stop word dictionary in Serbian

Marovac¹,

Avdić²,

Ljajić³

2021

Sci Pub Univ Novi Pazar Ser A

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A Survey of Resources and Methods for Natural Language Processing of Serbian Language

Marovac

Avdić

Milošević

2023

Preprint

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The Serbian language is a Slavic language spoken by over 12 million speakers and well understood by over 15 million people. In the area of natural language processing, it can be considered a low-resourced language. Also, Serbian is considered a high-inflectional language. The combination of many word inflections and low availability of language resources makes natural language processing of Serbian challenging. Nevertheless, over the past three decades, there have been a number of initiatives to develop resources and methods for natural language processing of Serbian, ranging from developing a corpus of free text from books and the internet, annotated corpora for classification and named entity recognition tasks to various methods and models performing these tasks. In this paper, we review the initiatives, resources, methods, and their availability.

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Towards Full Lexical Recognition

Cited by 2 publications

References 3 publications

Creating a stop word dictionary in Serbian

Creating a stop word dictionary in Serbian

A Survey of Resources and Methods for Natural Language Processing of Serbian Language

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