H
Hossein Nasr Esfahani
Researcher at University of Tehran
Publications - 7
Citations - 38
Hossein Nasr Esfahani is an academic researcher from University of Tehran. The author has contributed to research in topics: Prefix & String metric. The author has an hindex of 3, co-authored 6 publications receiving 26 citations.
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Book ChapterDOI
Authorship Identification Using Dynamic Selection of Features from Probabilistic Feature Set
Hamed Zamani,Hossein Nasr Esfahani,Pariya Babaie,Samira Abnar,Mostafa Dehghani,Azadeh Shakery +5 more
TL;DR: A probabilistic distribution model to represent each document as a feature set to increase the interpretability of the results and features is proposed and a distance measure is introduced to compute the distance between two feature sets.
Journal ArticleDOI
Impacts of disability on daily travel behaviour: A systematic review
Keun Pil Park,Hossein Nasr Esfahani,Valerie Novack,Jeff Sheen,Hooman Hadayeghi,Ziqi Song,Keith Christensen +6 more
TL;DR: In this article, the authors synthesize previous studies of travel behaviours among people with disabilities, differing from people without disabilities, in terms of trip frequency, mode choice, travel time and distance, and barriers.
Journal ArticleDOI
Building a multi-domain comparable corpus using a learning to rank method†
Razieh Rahimi,Azadeh Shakery,Javid Dadashkarimi,Mozhdeh Ariannezhad,Mostafa Dehghani,Hossein Nasr Esfahani +5 more
TL;DR: A learning to rank method for ranking candidate target documents with respect to each source document using a cross-lingual retrieval model is employed, constructed by defining each evidence for similarity of bilingual documents as a feature whose weight is learned automatically.
Profile-based Translation in Multilingual Expertise Retrieval.
TL;DR: It is shown that authors’ documents are usually related topically in different languages, and an effective profile-based translation model based on topicality of translations in other publications of the authors is provided.
Book ChapterDOI
SS4MCT: A Statistical Stemmer for Morphologically Complex Texts
TL;DR: A method is proposed for finding statistical inflectional rules based on minimum edit distance table of word pairs and the likelihoods of the rules in a language to statistically stem words and can be used in different text mining tasks.