Related papers: Development of a rule-based lemmatization algorith…
This work presents a morphological analyzer for the Uzbek language using a finite state machine. The proposed methodology is a morphologic analysis of Uzbek words by using an affix striping to find a root and without including any lexicon.…
In this paper we present a rule-based stemming algorithm for the Uzbek language. Uzbek is an agglutinative language, so many words are formed by adding suffixes, and the number of suffixes is also large. For this reason, it is difficult to…
As Uzbek language is agglutinative, has many morphological features which words formed by combining root and affixes. Affixes play an important role in the morphological analysis of words, by adding additional meanings and grammatical…
The accurate syllabification of words plays a vital role in various Natural Language Processing applications. Syllabification is a versatile linguistic tool with applications in linguistic research, language technology, education, and…
Lemmatization holds significance in both natural language processing (NLP) and linguistics, as it effectively decreases data density and aids in comprehending contextual meaning. However, due to the highly inflected nature and morphological…
We live in a translingual society, in order to communicate with people from different parts of the world we need to have an expertise in their respective languages. Learning all these languages is not at all possible; therefore we need a…
Stop words are very important for information retrieval and text analysis investigation tasks of natural language processing. Current work presents a method to evaluate the quality of a list of stop words aimed at automatically creating…
Lemmatization is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form. In computational linguistics, lemmatisation is the algorithmic…
Lemmatization is a natural language processing (NLP) task which consists of producing, from a given inflected word, its canonical form or lemma. Lemmatization is one of the basic tasks that facilitate downstream NLP applications, and is of…
Lemmatization is a Natural Language Processing (NLP) technique used to normalize text by changing morphological derivations of words to their root forms. It is used as a core pre-processing step in many NLP tasks including text indexing,…
This research paper presents a part-of-speech (POS) annotated dataset and tagger tool for the low-resource Uzbek language. The dataset includes 12 tags, which were used to develop a rule-based POS-tagger tool. The corpus text used in the…
In this paper, we share the process of developing word embeddings for the Cyrillic variant of the Uzbek language. The result of our work is the first publicly available set of word vectors trained on the word2vec, GloVe, and fastText…
Machine transliteration, as defined in this paper, is a process of automatically transforming written script of words from a source alphabet into words of another target alphabet within the same language, while preserving their meaning, as…
The smallest part of a word that defines the word is called a word root. Word roots are used to increase success in many applications since they simplify the word. In this study, the lemmatization model, which is a word root finding method,…
Lemmatization of standard languages is concerned with (i) abstracting over morphological differences and (ii) resolving token-lemma ambiguities of inflected words in order to map them to a dictionary headword. In the present paper we aim to…
Matching texts in highly inflected languages such as Arabic by simple stemming strategy is unlikely to perform well. In this paper, we present a strategy for automatic text matching technique for for inflectional languages, using Arabic as…
Lemmatization is crucial for NLP tasks in morphologically rich languages with ambiguous orthography like Arabic, but existing tools face challenges due to inconsistent standards and limited genre coverage. This paper introduces two novel…
Lemmatization is the task of transforming all words in a given text to their dictionary forms. While large language models (LLMs) have demonstrated their ability to achieve competitive results across a wide range of NLP tasks, there is no…
The volume of information is increasing at an incredible rate with the rapid development of the Internet and electronic information services. Due to time constraints, we don't have the opportunity to read all this information. Even the task…
This paper presents a method of stemming for the Arabian texts based on the linguistic techniques of the natural language processing. This method leans on the notion of scheme (one of the strong points of the morphology of the Arabian…