Demo Data from VLDB 2026 / reviewers in the wild / expert
Ainur Yessenalina
dblp:06/3748
· DBLP profile ↗
4ranked-venue papers
2as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 2 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
4 papers |
Information extraction and text analysis · 48% Language models and text generation · 20% Representation and self-supervised learning · 17% |
Topics — the 6 heaviest of 7, 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 |
0.2 | 2 | 2011 | Compositional Matrix-Space Models for Sentiment Analysis · EMNLP 2011 Multi-Level Structured Models for Document-Level Sentiment Classification · EMNLP 2010 |
Natural language and speech › Language models and text generation › text generation › text infilling
sentence infilling |
0.1 | 1 | 2012 | Computational Approaches to Sentence Completion · ACL (1) 2012 |
Natural language and speech › Information extraction and text analysis › sentiment analysis › sentiment classification
document-level sentiment classification |
0.1 | 1 | 2010 | Multi-Level Structured Models for Document-Level Sentiment Classification · EMNLP 2010 |
Machine learning › Learning paradigms
supervised learning |
0.1 | 1 | 2008 | An empirical evaluation of supervised learning in high dimensions · ICML 2008 |
Machine learning › Representation and self-supervised learning › word representation
compositional matrix-space model |
0.0 | 1 | 2011 | Compositional Matrix-Space Models for Sentiment Analysis · EMNLP 2011 |
Machine learning › Probabilistic and Bayesian machine learning
structured models |
0.0 | 1 | 2010 | Multi-Level Structured Models for Document-Level Sentiment Classification · EMNLP 2010 |
Methods — techniques the papers use, named apart from their topics
computational linguistics · 0.1matrix-space models · 0.1multi-level structured models · 0.1support vector machine · 0.1random forest · 0.1neural network · 0.1boosted trees · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Computational Approaches to Sentence Completion
Geoffrey Zweig, John C. Platt, Christopher Meek, Christopher J. C. Burges, Ainur Yessenalina, Qiang Liu 0001 |
ACL (1) | 5 |
| 2011 | Compositional Matrix-Space Models for Sentiment Analysis
Ainur Yessenalina, Claire Cardie |
EMNLP | 1 |
| 2010 | Multi-Level Structured Models for Document-Level Sentiment Classification
Ainur Yessenalina, Yisong Yue, Claire Cardie |
EMNLP | 1 |
| 2008 | An empirical evaluation of supervised learning in high dimensionsabstractIn this paper we perform an empirical evaluation of supervised learning on high-dimensional data. We evaluate perfor-mance on three metrics: accuracy, AUC, and squared loss and study the eect of increas-ing dimensionality on the performance of the learning algorithms. Our ndings are con-sistent with previous studies for problems of relatively low dimension, but suggest that as dimensionality increases the relative perfor-mance of the learning algorithms changes. To our surprise, the method that performs consistently well across all dimensions is ran-dom forests, followed by neural nets, boosted trees, and SVMs. 1. Rich Caruana, Nikolaos Karampatziakis, Ainur Yessenalina |
ICML | 3 |