A Survey on Similarity Measures in Text Mining

By V. Mk, K. K. 2016

397 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/15d06666358634cb7488e983f2ba9eae560d5655

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

1. Yongfeng Zhang
2. Liu Yang
3. W. Bruce Croft
4. Prem Devanbu
5. Chen Qu
6. Toufique Ahmed
7. Falk Scholer
8. Hamed Zamani

In Information Systems

1. Yongfeng Zhang
2. Liu Yang
3. W. Bruce Croft
4. Prem Devanbu
5. Chen Qu
6. Toufique Ahmed
7. Falk Scholer
8. Xu Chen

In Signal Processing

1. Prem Devanbu
2. W. Bruce Croft
3. Yongfeng Zhang
4. Toufique Ahmed

Citations in the corpus

• Answer Interaction in Non-factoid Question Answering Systems (2019, methodology): “ighted passage into a string by denoting the highlighted words as “1” with others as “0”. In this way the agreement of highlights can be cast into a string similarity problem. The overlap coefficient [28] is used to tackle this problem: overlap(H 1, H 2)= |H 1 ∩H 2| min(|H 1|, |H 2|) (1) where H1 an”
• Multilingual training for Software Engineering (2021, background): “(4) Calculate the mean Szymkiewicz-Simpson coefficient (overlap coefficient) [65] for both groups ( i.e. , same program pair and random pair) of programs.”