Measure of fuzzy presence of descriptors on Arabic Text Mining

Hassani, Ibtissam El; Kriouile, Abdelaziz; BenGhabrit, Youssef

doi:10.1109/cist.2012.6388063

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reference [17] presented two new textual feature selection methods based on Markov chains rank aggregation techniques. Reference [18] proposed a Markov chain model for radical descriptors in Arabic Text Mining. Reference [19] presented statistical Markov chain models for the distributions of words in text lines.…”

Section: Markov Chains Based Researchmentioning

confidence: 99%

A Survey of Markov Chain Models in Linguistics Applications

Al-Anziand¹,

AbuZeina²

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

Markov chain theory isan important tool in applied probability that is quite useful in modeling real-world computing applications.For a long time, rresearchers have used Markov chains for data modeling in a wide range of applications that belong to different fields such as computational linguists, image processing, communications,bioinformatics, finance systems, etc. This paper explores the Markov chain theory and its extension hidden Markov models (HMM) in natural language processing (NLP) applications. This paper also presents some aspects related to Markov chains and HMM such as creating transition matrices, calculating data sequence probabilities, and extracting the hidden states.

show abstract

Section: Markov Chains Based Researchmentioning

confidence: 99%

A Survey of Markov Chain Models in Linguistics Applications

Al-Anziand¹,

AbuZeina²

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

“…Reference [23] presented two new textual feature selection methods based on Markov chains rank aggregation techniques. Reference [24] proposed a Markov chain model for radical descriptors in Arabic text mining. Reference [25] presented statistical Markov chain models for the distributions of words in text lines.…”

Section: Nlp Markov Chains Based Researchmentioning

confidence: 99%