H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 38 Citations 7,037 191 World Ranking 5037 National Ranking 484

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Natural language processing

His main research concerns Artificial intelligence, Information retrieval, Natural language processing, Automatic summarization and Multi-document summarization. His Adversarial system study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Happiness, bridging the gap between disciplines. His Information retrieval research incorporates themes from Graph, Data mining, Cluster analysis, Ranking and Component.

His Natural language processing study frequently links to other fields, such as Leverage. His Automatic summarization research incorporates elements of Sentence and Graph based. His research integrates issues of Natural language generation, Word and Greedy algorithm in his study of Multi-document summarization.

His most cited work include:

  • Co-Training for Cross-Lingual Sentiment Classification (384 citations)
  • Single document keyphrase extraction using neighborhood knowledge (295 citations)
  • Multi-document summarization using cluster-based link analysis (254 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Artificial intelligence, Natural language processing, Information retrieval, Automatic summarization and Sentence. His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition. His work on Parsing, Cross lingual and Paraphrase as part of general Natural language processing research is frequently linked to Quality, bridging the gap between disciplines.

As part of the same scientific family, he usually focuses on Information retrieval, concentrating on Ranking and intersecting with Similitude. His study in Automatic summarization is interdisciplinary in nature, drawing from both Graph, Data mining and Leverage. In Data mining, Xiaojun Wan works on issues like Similarity, which are connected to Semantic similarity, Query expansion and Optimal matching.

He most often published in these fields:

  • Artificial intelligence (53.98%)
  • Natural language processing (42.92%)
  • Information retrieval (35.84%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (53.98%)
  • Natural language processing (42.92%)
  • Automatic summarization (27.88%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Natural language processing, Automatic summarization, Transformer and Parsing. His study ties his expertise on Machine learning together with the subject of Artificial intelligence. His Natural language processing research is multidisciplinary, incorporating perspectives in Set, Word, Utterance and Pun.

Automatic summarization is a component of his Multi-document summarization and Document summarization studies. His Transformer study integrates concerns from other disciplines, such as Autoencoder, Theoretical computer science, Text generation and Machine translation. The study incorporates disciplines such as Context and Vocabulary in addition to Information retrieval.

Between 2017 and 2021, his most popular works were:

  • SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks (93 citations)
  • A Neural Approach to Pun Generation (28 citations)
  • T-CVAE: transformer-based conditioned variational autoencoder for story completion (24 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Natural language processing

Xiaojun Wan focuses on Artificial intelligence, Natural language processing, Transformer, Automatic summarization and Benchmark. Much of his study explores Artificial intelligence relationship to Machine learning. His Natural language processing study combines topics from a wide range of disciplines, such as Word, Grammar and Readability.

Particularly relevant to Multi-document summarization is his body of work in Automatic summarization. His Sentence research is multidisciplinary, incorporating elements of Granularity and Leverage. The Domain study combines topics in areas such as Sentiment analysis, Embedding and Information retrieval.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Co-Training for Cross-Lingual Sentiment Classification

Xiaojun Wan.
international joint conference on natural language processing (2009)

574 Citations

Single document keyphrase extraction using neighborhood knowledge

Xiaojun Wan;Jianguo Xiao.
national conference on artificial intelligence (2008)

412 Citations

Multi-document summarization using cluster-based link analysis

Xiaojun Wan;Jianwu Yang.
international acm sigir conference on research and development in information retrieval (2008)

354 Citations

Manifold-ranking based topic-focused multi-document summarization

Xiaojun Wan;Jianwu Yang;Jianguo Xiao.
international joint conference on artificial intelligence (2007)

286 Citations

Using Bilingual Knowledge and Ensemble Techniques for Unsupervised Chinese Sentiment Analysis

Xiaojun Wan.
empirical methods in natural language processing (2008)

244 Citations

Abstractive Document Summarization with a Graph-Based Attentional Neural Model

Jiwei Tan;Xiaojun Wan;Jianguo Xiao.
meeting of the association for computational linguistics (2017)

237 Citations

Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction

Xiaojun Wan;Jianwu Yang;Jianguo Xiao.
meeting of the association for computational linguistics (2007)

217 Citations

Evolutionary timeline summarization: a balanced optimization framework via iterative substitution

Rui Yan;Xiaojun Wan;Jahna Otterbacher;Liang Kong.
international acm sigir conference on research and development in information retrieval (2011)

203 Citations

Attention-based LSTM Network for Cross-Lingual Sentiment Classification

Xinjie Zhou;Xiaojun Wan;Jianguo Xiao.
empirical methods in natural language processing (2016)

187 Citations

CollabRank: Towards a Collaborative Approach to Single-Document Keyphrase Extraction

Xiaojun Wan;Jianguo Xiao.
international conference on computational linguistics (2008)

142 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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Top Scientists Citing Xiaojun Wan

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