Stop-word removal algorithm for arabic language

Al-Shalabi, Riyad; Kanaan, Ghassan; Jaam, Jihad Mohamad; Hasnah, Ahmad; Hilat, E.

doi:10.1109/ictta.2004.1307875

Cited by 32 publications

(32 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the authors of [14] show the effect of not using capital letters in Arabic words, which makes it hard to identify proper names, abbreviations and as a result it would makes it complicated in tasks such as in Information Extraction and Named Entity Recognition.…”

Section: The Arabic Language Features and Challengesmentioning

confidence: 99%

See 1 more Smart Citation

Classifying Arabic Text Using KNN Classifier

Al-Badarenah¹,

Al-Shawakfa²,

Al-Rababah³

et al. 2016

ijacsa

View full text Add to dashboard Cite

Abstract-With the tremendous amount of electronic documents available, there is a great need to classify documents automatically. Classification is the task of assigning objects (images, text documents, etc.) to one of several predefined categories. The selection of important terms is vital to classifier performance, feature set reduction techniques such as stop word removal, stemming and term threshold were used in this paper. Three term-selection techniques are used on a corpus of 1000 documents that fall in five categories. A comparison study is performed to find the effect of using full-word, stem, and the root term indexing methods. K-nearest -neighbors classifiers used in this study. The averages of all folds for Recall, Precision, Fallout, and Error-Rate were calculated. The results of the experiments carried out on the dataset show the importance of using k-fold testing since it presents the variations of averages of recall, precision, fallout, and error rate for each category over the 10-fold.

show abstract

Section: The Arabic Language Features and Challengesmentioning

confidence: 99%

“…Stemming is a computational process that collects all words which share the same stem and have the some semantic relation [14]. The goal of the stemming process is to remove all possible affixes, so as a result reducing the word to its stem.…”

Section: Statistical Stemmersmentioning

confidence: 99%

Classifying Arabic Text Using KNN Classifier

Al-Badarenah¹,

Al-Shawakfa²,

Al-Rababah³

et al. 2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…Then, The Arabic stop words are removed. Some Arabic documents may contain foreign words, special characters, numbers [23,24]. Finally, words with length less than three letters are eliminated, often these words are not important and are not useful in TC.…”

Section: A Text Preprocessingmentioning

confidence: 99%

Arabic Text Categorization Using Mixed Words

Hussein¹,

Mousa²,

Sallam³

2016

IJITCS

View full text Add to dashboard Cite

Abstract-There is a tremendous number of Arabic text documents available online that is growing every day. Thus, categorizing these documents becomes very important. In this paper, an approach is proposed to enhance the accuracy of the Arabic text categorization. It is based on a new features representation technique that uses a mixture of a bag of words (BOW) and two adjacent words with different proportions. It also introduces a new features selection technique depends on Term Frequency (TF) and uses Frequency Ratio Accumulation Method (FRAM) as a classifier. Experiments are performed without both of normalization and stemming, with one of them, and with both of them. In addition, three data sets of different categories have been collected from online Arabic documents for evaluating the proposed approach. The highest accuracy obtained is 98.61% by the use of normalization.

show abstract

“…In [14] an algorithm for removing stopwords have been proposed which is based on a finite state machine in Arabic language. A hybrid stopword removal technique was implemented based on a dictionary and an algorithm for Arabic language [15].…”

Section: Related Workmentioning

confidence: 99%

HSRA: Hindi stopword removal algorithm

Jha

Manjunath

Shenoy

et al. 2016

2016 International Conference on Microelectronics, Computing and Communications (MicroCom)

View full text Add to dashboard Cite

Abstract-In the last few years, electronic documents have been the main source of data in many research areas like Web Mining, Information Retrieval, Artificial Intelligence, Natural Language Processing etc.. Text Processing plays a vital role for processing structured or unstructured data from the web. Preprocessing is the main step in any text processing systems. One significant preprocessing technique is the elimination of functional words, also known as stopwords, which affects the performance of text processing tasks. An efficient stopword removal technique is required in all text processing tasks. In this paper, we are proposing a stopword removal algorithm for Hindi Language which is using the concept of a Deterministic Finite Automata (DFA). A large number of available works on stopword removal techniques are based on dictionary containing stopword lists. Then pattern matching techique is applied and the matched patterns, which is a stopword, is removed from the document. It is a time consuming task as searching process takes a long time. This makes the method inefficient and very expensive. In comparision of that, our algorithm has been tested on 200 documents and achieved 99% accuracy and also time efficient.

show abstract

Stop-word removal algorithm for arabic language

Cited by 32 publications

References 0 publications

Classifying Arabic Text Using KNN Classifier

Classifying Arabic Text Using KNN Classifier

Arabic Text Categorization Using Mixed Words

HSRA: Hindi stopword removal algorithm

Contact Info

Product

Resources

About