irBlogs: A standard collection for studying Persian bloggers

AleAhmad, Abolfazl; Zahedi, Mohammad Sadegh; Rahgozar, Maseud; Moshiri, Behzad

doi:10.1016/j.chb.2015.11.038

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…In the experiments, we adopt two unexplored social network datasets, that is, irBlogs [45] and Telegram. Each of them is directed and contains information on two aspects: the network topology and the user-generated contents.…”

Section: Resultsmentioning

confidence: 99%

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DeepLink: A novel link prediction framework based on deep learning

Keikha

Rahgozar

Asadpour

2019

Journal of Information Science

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Recently, link prediction has attracted more attentions from various disciplines such as computer science, bioinformatics and economics. In this problem, unknown links between nodes are discovered based on numerous information such as network topology, profile information and user generated contents. Most of the previous researchers have focused on the structural features of the networks. While the recent researches indicate that contextual information can change the network topology. Although, there are number of valuable researches which combine structural and content information, but they face with the scalability issue due to feature engineering. Because, majority of the extracted features are obtained by a supervised or semi supervised algorithm. Moreover, the existing features are not general enough to indicate good performance on different networks with heterogeneous structures. Besides, most of the previous researches are presented for undirected and unweighted networks.In this paper, a novel link prediction framework called "DeepLink" is presented based on deep learning techniques. In contrast to the previous researches which fail to automatically extract best features for the link prediction, deep learning reduces the manual feature engineering. In this framework, both the structural and content information of the nodes are employed. The framework can use different structural feature vectors, which are prepared by various link prediction methods. It considers all proximity orders that are presented in a network during the structural feature learning. We have evaluated the performance of DeepLink on two real social network datasets including Telegram and irBlogs. On both datasets, the proposed framework outperforms several structural and hybrid approaches for link prediction problem.

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Section: Resultsmentioning

confidence: 99%

“…IrBlogs dataset [45]: This dataset contains nearly 5 million posts and the network of more than 560,000 Persian bloggers. In this dataset, each node is a blog, and the edge ( u , v ) shows that node u shares w contents that are generated by node v .…”

Section: Resultsmentioning

confidence: 99%

DeepLink: A novel link prediction framework based on deep learning

Keikha

Rahgozar

Asadpour

2019

Journal of Information Science

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“…In order to evaluate and compare the proposed time-sensitive blog retrieval methods, we use the irBlogs dataset [43]. This collection is used as a basis for some other researches [44, 45].…”

Section: Methodsmentioning

confidence: 99%

“…TEMPER Propose a framework, TEMPER, which selects different terms for different times and ranks blogs according to their relevancy to the query over time [42]. Relevance Stability (RS) Propose a probabilistic framework to measure the stability of blogs relevance over time [43]. TSV…”

Section: Name Descriptionmentioning

confidence: 99%

Time sensitive blog retrieval using temporal properties of queries

Zahedi

AleAhmad

Rahgozar

et al. 2016

Journal of Information Science

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Blogs are one of the main user-generated contents on the web and are growing in number rapidly. The characteristics of blogs require the development of specialized search methods which are tuned for the blogosphere. In this paper, we focus on blog retrieval, which aims at ranking blogs with respect to their recurrent relevance to a user's topic. Although different blog retrieval algorithms have already been proposed, few of them have considered temporal properties of the input queries. Therefore, we propose an efficient approach to improving relevant blog retrieval using temporal property of queries. First, time sensitivity of each query is automatically computed for different time intervals based on an initially retrieved set of relevant posts. Then a temporal score is calculated for each blog and finally all blogs are ranked based on their temporal and content relevancy with regard to the input query. Experimental analysis and comparison of the proposed method are carried out using a standard dataset with 45 diverse queries. Our experimental results demonstrate that, using different measurement criteria, our proposed method outperforms other blog retrieval methods.

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“…irBlogs (AleAhmad et al, 2016) is a collection of 5M+ posts from 600K+ Persian weblogs. Some blogs use informal language for their posts, so this dataset has an enormous amount of informal texts, which could help our models become familiar with this type of Persian speech.…”

Section: Pre-training Datasetsmentioning

confidence: 99%

ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization

Salemi¹,

Kebriaei²,

Minaei³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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ive text summarization is one of the areas influenced by the emergence of pre-trained language models. Current pre-training works in abstractive summarization give more points to the summaries with more words in common with the main text and pay less attention to the semantic similarity between generated sentences and the original document. We propose ARMAN, a Transformer-based encoderdecoder model pre-trained with three novel objectives to address this issue. In ARMAN, salient sentences from a document are selected according to a modified semantic score to be masked and form a pseudo summary. To summarize more accurately and similar to human writing patterns, we applied modified sentence reordering. We evaluated our proposed models on six downstream Persian summarization tasks. Experimental results show that our proposed model achieves state-of-the-art performance on all six summarization tasks measured by ROUGE and BERTScore. Our models also outperform prior works in textual entailment, question paraphrasing, and multiple choice question answering. Finally, we established a human evaluation and show that using the semantic score significantly improves summarization results.

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irBlogs: A standard collection for studying Persian bloggers

Cited by 9 publications

References 16 publications

DeepLink: A novel link prediction framework based on deep learning

DeepLink: A novel link prediction framework based on deep learning

Time sensitive blog retrieval using temporal properties of queries

ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization

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