Human-competitive tagging using automatic keyphrase extraction
By Olena Medelyan, Eibe Frank, Ian H. Witten. Conference on Empirical Methods in Natural Language Processing, 2009
361 citations recorded by the source corpus.
This paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. First, we analyze the quality of tags in a collaboratively created folksonomy using traditional evaluation techniques. Next, we demonstrate how documents can be tagged automatically with a state-of-the-art keyphrase extraction algorithm, and further improve performance in this new domain using a new algorithm, "Maui", that utilizes semantic information extracted from Wikipedia. Maui outperforms existing approaches and extracts tags that are competitive with those assigned by the best performing human taggers.
Subfields: Artificial Intelligence, Information Systems, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/851e7b462e225414aa5dda7f626055e3d98bfe2b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Adam Trischler
- Yoshua Bengio
- Mohit Bansal
- Xingdi Yuan
- Sandeep Subramanian
- Jianfeng Gao
- Tong Wang
- Percy Liang
In Information Systems
- Vincent Ng
- Tong Wang
- Cornelia Caragea
- Yoshua Bengio
- Kazi Saidul Hasan
- Xingdi Yuan
- Adam Trischler
- Corina Florescu
In Molecular Biology
- Yoshua Bengio
- Vincent Ng
- Tong Wang
- Cornelia Caragea
- Kazi Saidul Hasan
- Sandeep Subramanian
- Corina Florescu
- Stefano Ermon
Citations in the corpus
- Automatic Keyphrase Extraction: A Survey of the State of the Art (2014, methodology): “…a sample of their systems’ output and the corresponding gold keyphrases to show the differences between them (Witten et al., 1999; Nguyen and Kan, 2007; Medelyan et al., 2009), a systematic analysis of the major types of errors made by state-of-the-art keyphrase extraction systems is missing.”
- Neural Models for Key Phrase Extraction and Question Generation (2018, methodology): “Medelyan et al. (2009); Lopez and Romary (2010) used bagged decision trees, while Lopez and Romary (2010) used a Multi-Layer Perceptron (MLP) and Support Vector Machine to perform binary classification on the candidates.”
- PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents (2017, background): “In a different work, Medelyan et al. (2009) extended KEA to integrate information from Wikipedia.”