VLDB 2026 Research / reviewers in the wild / expert
Erfan Ghadery
dblp:232/1721
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › sentiment analysis
aspect-based sentiment analysis |
0.4 | 1 | 2019 | MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews · AAAI 2019 |
Natural language and speech › Information extraction and text analysis › sentiment analysis › aspect-based sentiment analysis
aspect category detection |
0.4 | 1 | 2019 | MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews · AAAI 2019 |
Natural language and speech › Information extraction and text analysis
multilingual NLP |
0.4 | 1 | 2019 | MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews · AAAI 2019 |
Methods — techniques the papers use, named apart from their topics
multilingual word embeddings · 0.4convolutional neural network · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online ReviewsabstractThe advent of the Internet has caused a significant growth in the number of opinions expressed about products or services on e-commerce websites. Aspect category detection, which is one of the challenging subtasks of aspect-based sentiment analysis, deals with categorizing a given review sentence into a set of predefined categories. Most of the research efforts in this field are devoted to English language reviews, while there are a large number of reviews in other languages that are left unexplored. In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings. To the best of our knowledge, our method is the first attempt at performing aspect category detection on multiple languages simultaneously. Empirical results on the multilingual dataset provided by SemEval workshop demonstrate the effectiveness of the proposed method1. Erfan Ghadery, Sajad Movahedi, Heshaam Faili, Azadeh Shakery |
AAAI | 1 |
| 2019 | LICD: A Language-Independent Approach for Aspect Category Detection
Erfan Ghadery, Sajad Movahedi, Masoud Jalili Sabet, Heshaam Faili, Azadeh Shakery |
ECIR (1) | 1 |