Highly Language-Independent Word Lemmatization Using a Machine-Learning Classifier

Akhmetov, Iskander; Pak, Alexandr; Ualiyeva, Irina; Gelbukh, Alexander

doi:10.13053/cys-24-3-3775

Cited by 18 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is the process of converting a word into a normalized form. It consists of removing the suffix of a word [57,58]. For instance, by removing the words' suffixes, ranked, and ranks, we get the lemma rank.…”

Section: Lemmatizationmentioning

confidence: 99%

Intelligent Recommendations of Startup Projects in Smart Cities and Smart Health Using Social Media Mining

Karâa

Alkhammash

Slimani

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

The paper presents a recommendation model for developing new smart city and smart health projects. The objective is to provide recommendations to citizens about smart city and smart health startups to improve entrepreneurship and leadership. These recommendations may lead to the country’s advancement and the improvement of national income and reduce unemployment. This work focuses on designing and implementing an approach for processing and analyzing tweets inclosing data related to smart city and smart health startups and providing recommended projects as well as their required skills and competencies. This approach is based on tweets mining through a machine learning method, the Word2Vec algorithm, combined with a recommendation technique conducted via an ontology-based method. This approach allows discovering the relevant startup projects in the context of smart cities and makes links to the needed skills and competencies of users. A system was implemented to validate this approach. The attained performance metrics related to precision, recall, and F-measure are, respectively, 95%, 66%, and 79%, showing that the results are very encouraging.

show abstract

Section: Lemmatizationmentioning

confidence: 99%

Intelligent Recommendations of Startup Projects in Smart Cities and Smart Health Using Social Media Mining

Karâa

Alkhammash

Slimani

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…A rule-based stemmer was also introduced for the Sinhala language using prefix and suffix rule. There are a number of preexisting stemmers but this rulebased stemmer is capable to generate correct root words [41].…”

Section: Different Preprocessing Techniques Provide Different Classif...mentioning

confidence: 99%

Improvisation in opinion mining using data preprocessing techniques based on consumer’s review

2023

IJATEE

View full text Add to dashboard Cite

show abstract

“…There are a number of lemmatization approaches: rule-based, simple statistical-based methods, and machine learning-based methods (Akhmetov et al, 2020).…”

Section: Lemmatization and Part-of-speech Taggingmentioning

confidence: 99%

Creating a stop word dictionary in Serbian

Marovac¹,

Avdić²,

Ljajić³

2021

Sci Pub Univ Novi Pazar Ser A

View full text Add to dashboard Cite

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.

show abstract

Highly Language-Independent Word Lemmatization Using a Machine-Learning Classifier

Cited by 18 publications

References 0 publications

Intelligent Recommendations of Startup Projects in Smart Cities and Smart Health Using Social Media Mining

Intelligent Recommendations of Startup Projects in Smart Cities and Smart Health Using Social Media Mining

Improvisation in opinion mining using data preprocessing techniques based on consumer’s review

Creating a stop word dictionary in Serbian

Contact Info

Product

Resources

About