A Comparison of Text String Similarity Algorithms for POI Name Harmonisation

2018 ◽  
pp. 121-130 ◽  
Author(s):  
Jiří Kysela
Keyword(s):  
String Similarity ◽  
Text String

scholarly journals Text String Detection From Natural Scenes by Structure-Based Partition and Grouping

2011 ◽  
Vol 20 (9) ◽  
pp. 2594-2605 ◽  
Author(s):  
Chucai Yi ◽  
YingLi Tian
Keyword(s):  
Natural Scenes ◽  
Text String

scholarly journals UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures

2016 ◽  
Author(s):  
Timothy Baldwin ◽  
Huizhi Liang ◽  
Bahar Salehi ◽  
Doris Hoogeveen ◽  
Yitong Li ◽  
...  
Keyword(s):  
Similarity Measures ◽  
String Similarity ◽  
String Similarity Measures

Efficient and Scalable Processing of String Similarity Join

2013 ◽  
Vol 25 (10) ◽  
pp. 2217-2230 ◽  
Author(s):  
Chuitian Rong ◽  
Wei Lu ◽  
Xiaoli Wang ◽  
Xiaoyong Du ◽  
Yueguo Chen ◽  
...  
Keyword(s):  
Similarity Join ◽  
String Similarity

A New LCS-Neutrosophic Similarity Measure for Text Information Retrieval

2020 ◽  
pp. 258-280
Author(s):  
Misturah Adunni Alaran ◽  
AbdulAkeem Adesina Agboola ◽  
Adio Taofiki Akinwale ◽  
Olusegun Folorunso
Keyword(s):  
Information Retrieval ◽  
Similarity Measure ◽  
Information Search ◽  
Longest Common Subsequence ◽  
Data Set ◽  
String Similarity ◽  
True Match ◽  
Neutrosophic Logic ◽  
Common Subsequence ◽  
Text Information

The reality of human existence and their interactions with various things that surround them reveal that the world is imprecise, incomplete, vague, and even sometimes indeterminate. Neutrosophic logic is the only theory that attempts to unify all previous logics in the same global theoretical framework. Extracting data from a similar environment is becoming a problem as the volume of data keeps growing day-in and day-out. This chapter proposes a new neutrosophic string similarity measure based on the longest common subsequence (LCS) to address uncertainty in string information search. This new method has been compared with four other existing classical string similarity measure using wordlist as data set. The analyses show the performance of proposed neutrosophic similarity measure to be better than the existing in information retrieval task as the evaluation is based on precision, recall, highest false match, lowest true match, and separation.

Para-Join: an efficient parallel method for string similarity join

2017 ◽  
Vol 10 (4/5) ◽  
pp. 381
Author(s):  
Cairong Yan ◽  
Jian Wang ◽  
Bin Zhu ◽  
Wenjing Guo
Keyword(s):  
Parallel Method ◽  
Similarity Join ◽  
String Similarity

A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses

2011 ◽  
pp. 745-759
Author(s):  
Hamid Haidarian Shahri
Keyword(s):  
Fuzzy Inference ◽  
Data Cleaning ◽  
Similarity Measures ◽  
Entity Resolution ◽  
String Similarity ◽  
Domain Specific ◽  
Meta Level ◽  
Neuro Fuzzy ◽  
Resolution Problem ◽  
String Similarity Measures

Entity resolution (also known as duplicate elimination) is an important part of the data cleaning process, especially in data integration and warehousing, where data are gathered from distributed and inconsistent sources. Learnable string similarity measures are an active area of research in the entity resolution problem. Our proposed framework builds upon our earlier work on entity resolution, in which fuzzy rules and membership functions are defined by the user. Here, we exploit neuro-fuzzy modeling for the first time to produce a unique adaptive framework for entity resolution, which automatically learns and adapts to the specific notion of similarity at a meta-level. This framework encompasses many of the previous work on trainable and domain-specific similarity measures. Employing fuzzy inference, it removes the repetitive task of hard-coding a program based on a schema, which is usually required in previous approaches. In addition, our extensible framework is very flexible for the end user. Hence, it can be utilized in the production of an intelligent tool to increase the quality and accuracy of data.

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