A Comparative Study of Text Preprocessing Techniques for Natural Language Call Routing

2016 ◽  
pp. 23-37 ◽  
Author(s):  
Roman Sergienko ◽  
Muhammad Shan ◽  
Alexander Schmitt
Keyword(s):  
Natural Language ◽  
Comparative Study ◽  
Call Routing ◽  
Text Preprocessing

Natural Language Compression on Edge-Guided text preprocessing

2011 ◽  
Vol 181 (24) ◽  
pp. 5387-5411 ◽  
Author(s):  
Miguel A. Martínez-Prieto ◽  
Joaquín Adiego ◽  
Pablo de la Fuente
Keyword(s):  
Natural Language ◽  
Text Preprocessing

Boosting and combination of classifiers for natural language call routing systems

2003 ◽  
Vol 41 (4) ◽  
pp. 647-661 ◽  
Author(s):  
Imed Zitouni ◽  
Hong-Kwang Jeff Kuo ◽  
Chin-Hui Lee
Keyword(s):  
Natural Language ◽  
Call Routing ◽  
Combination Of Classifiers

scholarly journals Using Natural Language Preprocessing Architecture (NLPA) for Big Data Text Sources

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
María Novo-Lourés ◽  
Reyes Pavón ◽  
Rosalía Laza ◽  
David Ruano-Ordas ◽  
Jose R. Méndez
Keyword(s):  
Big Data ◽  
Natural Language ◽  
Semantic Information ◽  
Application Programming Interface ◽  
Unstructured Data ◽  
Application Programming ◽  
Text Preprocessing ◽  
Programming Interface ◽  
Proper Analysis

During the last years, big data analysis has become a popular means of taking advantage of multiple (initially valueless) sources to find relevant knowledge about real domains. However, a large number of big data sources provide textual unstructured data. A proper analysis requires tools able to adequately combine big data and text-analysing techniques. Keeping this in mind, we combined a pipelining framework (BDP4J (Big Data Pipelining For Java)) with the implementation of a set of text preprocessing techniques in order to create NLPA (Natural Language Preprocessing Architecture), an extendable open-source plugin implementing preprocessing steps that can be easily combined to create a pipeline. Additionally, NLPA incorporates the possibility of generating datasets using either a classical token-based representation of data or newer synset-based datasets that would be further processed using semantic information (i.e., using ontologies). This work presents a case study of NLPA operation covering the transformation of raw heterogeneous big data into different dataset representations (synsets and tokens) and using the Weka application programming interface (API) to launch two well-known classifiers.

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