Hossein Sameti

Also published as: H. Sameti


pdf bib
Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification
Ehsan Doostmohammadi | Hossein Sameti | Ali Saffar
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and categorizing offensive language in social media in three subtasks; whether or not a content is offensive (subtask A), whether it is targeted (subtask B) towards an individual, a group, or other entities (subtask C). The proposed approach includes character-level Convolutional Neural Network, word-level Recurrent Neural Network, and some preprocessing. The performance achieved by the proposed model is 77.93% macro-averaged F1-score.


pdf bib
SUT System Description for Anti-Spoofing 2017 Challenge
Mohammad Adiban | Hossein Sameti | Noushin Maghsoodi | Sajjad Shahsavari
Proceedings of the 29th Conference on Computational Linguistics and Speech Processing (ROCLING 2017)

pdf bib
SUT Submission for NIST 2016 Speaker Recognition Evaluation: Description and Analysis
Hossein Zeinali | Hossein Sameti | Noushin Maghsoodi
Proceedings of the 29th Conference on Computational Linguistics and Speech Processing (ROCLING 2017)


pdf bib
Active Learning in Noisy Conditions for Spoken Language Understanding
Hossein Hadian | Hossein Sameti
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


pdf bib
Building and Incorporating Language Models for Persian Continuous Speech Recognition Systems
M. Bahrani | H. Sameti | N. Hafezi | H. Movasagh
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In this paper building statistical language models for Persian language using a corpus and incorporating them in Persian continuous speech recognition (CSR) system are described. We used Persian Text Corpus for building the language models. First we preprocessed the texts of corpus by correcting the different orthography of words. Also, the number of POS tags was decreased by clustering POS tags manually. Then we extracted word based monogram and POS-based bigram and trigram language models from the corpus. We also present the procedure of incorporating language models in a Persian CSR system. By using the language models 27.4% reduction in word error rate was achieved in the best case.