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SentiPers: A Sentiment Analysis Corpus for Persian
- Pedram Hosseini, Ali Ahmadian Ramaki, H. Maleki, Mansoureh Anvari, Seyed Abolghasem Mirroshandel
- Computer ScienceArXiv
- 23 January 2018
This paper outlines the entire process of developing a manually annotated sentiment corpus, SentiPers, which covers formal and informal written contemporary Persian and is a unique sentiment corpus with such a rich annotation in three different levels including document-level, sentence- level, and entity/aspect-level for Persian.
LexiPers: An ontology based sentiment lexicon for Persian
- Behnam Sabeti, Pedram Hosseini, Gholamreza Ghassem-Sani, Seyed Abolghasem Mirroshandel
- Computer ScienceGCAI
- 29 September 2016
The process of generating a general purpose sentiment lexicon for Persian using the K-nearest neighbors and nearest centroid methods for classification and a new graph-based method is introduced for seed selection and expansion based on an ontology.
ParsiNLU: A Suite of Language Understanding Challenges for Persian
- Daniel Khashabi, Arman Cohan, +22 authors Yadollah Yaghoobzadeh
- Computer ScienceTransactions of the Association for Computational…
- 11 December 2020
This work introduces ParsiNLU, the first benchmark in Persian language that includes a range of language understanding tasks—reading comprehension, textual entailment, and so on, and presents the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compares them with human performance.
Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP
- Pedram Hosseini, Poorya Hosseini, David A. Broniatowski
- Computer Science, HistoryNLP4COVID@EMNLP
- 17 May 2020
This study analyzed the topics discussed among users of Twitter to gauge and track the response to the pandemic and how it evolved over time, and identified the top 25 topics among which living experience under home quarantine emerged as a major talking point.
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
- Or Levi, Pedram Hosseini, Mona T. Diab, David A. Broniatowski
- Computer Science, PsychologyEMNLP
- 2 October 2019
This work addresses the challenge of automatically classifying fake news versus satire with a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics.
A Multi-Modal Method for Satire Detection using Textual and Visual Cues
This work creates a new dataset consisting of images and headlines of regular and satirical news for the task of satire detection, and proposes a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT.
Does Causal Coherence Predict Online Spread of Social Media?
- Pedram Hosseini, Mona T. Diab, David A. Broniatowski
- Computer Science, PsychologySBP-BRiMS
- 9 July 2019
Test the hypothesis that causal and semantic coherence are associated with online sharing of misinformative social media content using Coh-Metrix – a widely-used set of psycholinguistic measures to support Fuzzy-Trace Theory’s predictions regarding the role of causally- and semantically-coherent content and motivate better measures of these key constructs.
Predicting Directionality in Causal Relations in Text
This work test the performance of two bidirectional transformer-based language models, BERT and SpanBERT, on predicting directionality in causal pairs in the textual content and introduces CREST, a framework for unifying a collection of scattered datasets of causal relations.
Automatically Identifying Political Ads on Facebook: Towards Understanding of Manipulation via User Targeting
This work studies the political ads dataset collected by ProPublica using a network of volunteers in the period before the 2018 US midterm elections, and addresses the challenge of automatically classifying between political and non-political ads, and investigates whether the user targeting attributes are beneficial for this task.
Commonsense Knowledge-Augmented Pretrained Language Models for Causal Reasoning Classification
The results show that a continually pretrained language model augmented with commonsense reasoning knowledge outperforms the authors' baseline on two commonsense causal reasoning benchmarks, COPA and BCOPA-CE, without additional improvement on the base model or using quality-enhanced data for finetuning.