Effect of stemming on text similarity for Arabic language at sentence level
نویسندگان
چکیده
منابع مشابه
The Effect of Stemming on Arabic Text Classification: An Empirical Study
The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to E...
متن کاملRational Kernels for Arabic Stemming and Text Classification
In this paper, we address the problems of Arabic Text Classification and stemming using Transducers and Rational Kernels. We introduce a new stemming technique based on the use of Arabic patterns (Pattern Based Stemmer). Patterns are modelled using transducers and stemming is done without depending on any dictionary. Using transducers for stemming, documents are transformed into finite state tr...
متن کاملMeasuring the sentence level similarity
This article describes a method used to calculate the similarity between short English texts, specifically of sentence length. The described algorithm calculates semantic and word order similarities of two sentences. In order to do so, it uses a structured lexical knowledge base and statistical information from a corpus. The described method works well in determining sentence similarity for mos...
متن کاملImproved Sentence-Level Arabic Dialect Classification
The paper presents work on improved sentence-level dialect classification of Egyptian Arabic (ARZ) vs. Modern Standard Arabic (MSA). Our approach is based on binary feature functions that can be implemented with a minimal amount of task-specific knowledge. We train a featurerich linear classifier based on a linear support-vector machine (linear SVM) approach. Our best system achieves an accurac...
متن کاملSentence Level Dialect Identification in Arabic
This paper introduces a supervised approach for performing sentence level dialect identification between Modern Standard Arabic and Egyptian Dialectal Arabic. We use token level labels to derive sentence-level features. These features are then used with other core and meta features to train a generative classifier that predicts the correct label for each sentence in the given input text. The sy...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PeerJ Computer Science
سال: 2021
ISSN: 2376-5992
DOI: 10.7717/peerj-cs.530