DKPro Similarity: An Open Source Framework for Text Similarity

By Daniel Bär, Torsten Zesch, Iryna Gurevych. 2013

94 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Mohit Bansal
2. Julia Hockenmaier
3. Micah Hodosh
4. Jianfeng Gao
5. Yejin Choi
6. Percy Liang
7. Luke S. Zettlemoyer
8. Tommi S. Jaakkola

In Evaluation & Benchmarks

1. Alessandro Moschitti
2. Julia Hockenmaier
3. Peter Young
4. Micah Hodosh
5. Benno Stein
6. Steven Burrows
7. Kateryna Tymoshenko
8. Iryna Gurevych

Citations in the corpus

• From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions (2014, methodology): “Baseline and Compositional Features Our starting point is B¨ar et al. (2013)’s DKPro Similarity, one of the top-performing models from the 2012 STS shared task, which is available and easily mod-ified.”
• Semi-supervised Question Retrieval with Gated Convolutions (2015, methodology): “• SVM , we trained a re-ranker using SVM-Light (Joachims, 2002) with a linear kernel incorporating several similarity measures from the DKPro similarity package (B¨ar et al., 2013).”
• The Eras and Trends of Automatic Short Answer Grading (2015, methodology): “) is based on combining multiple textual similarity measures together using DKPro Similarity (Bär et al. 2013) and BIUTEE (Stern and Dagan 2011) as established technology for textual similarity and entailment respectively.”
• Denoising Bodies to Titles: Retrieving Similar Questions with Recurrent Convolutional Models (2015, methodology): “• SVM, we trained a re-ranker using SVM-Light (Joachims, 2002) with a linear kernel incorporating various similarity measures from the DKPro similarity package (Bär et al., 2013).”