research-article

PicAChoo: a tool for customizable feature extraction utilizing characteristics of textual data

Authors:

Jaeseok Myung,

Jung-Yeon Yang,

Sang-goo LeeAuthors Info & Claims

ICUIMC '09: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication

Pages 650 - 655

https://doi.org/10.1145/1516241.1516356

Published: 15 February 2009 Publication History

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Abstract

Although documents have hundreds of thousands of unique words, only a small number of words are significantly useful for intelligent services. For this reason, feature extraction has become an important issue to be addressed in various fields, such as information retrieval, text mining, pattern recognition, etc. Numerous supporting tools for feature extraction are available, but most of them deal with text as a simple literal. Unfortunately, text is not just a literal, but a semantically significant unit including linguistic characteristics. So, we need customized extraction methods that consider the characteristics of source documents. PicAChoo stands for 'Pick And Choose', and it provides an environment which enables feature extraction methods using the structure of sentences and the part-of-speech information of words. Moreover, we suggest dynamic composition of different extraction methods without hard-coding.

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http://ids.snu.ac.kr/wiki/PicAChoo_%28Pick_And_Choose%29

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Cited By

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Elagamy MStanier CSharp B(2018)Stock market random forest-text mining system mining critical indicators of stock market movements2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP)10.1109/ICNLSP.2018.8374370(1-8)Online publication date: Apr-2018
https://doi.org/10.1109/ICNLSP.2018.8374370
Elagamy MStanier CSharp B(2018)Text Mining Approach to Analyse Stock Market MovementThe International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018)10.1007/978-3-319-74690-6_65(661-670)Online publication date: 26-Jan-2018
https://doi.org/10.1007/978-3-319-74690-6_65

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PicAChoo: a tool for customizable feature extraction utilizing characteristics of textual data

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Reviews

Reviewer: Julien Velcin

In many applications that use text analysis, feature extraction is one of the most significant tasks. PicAChoo, the tool proposed in this paper, is a user-centered environment that considers characteristics of languages, such as structure and part of speech (POS). Unlike other widely used methods, such as RapidMiner or Weka, this environment is flexible, semi-automatic, and supports explicitly complex features through the notion of context. It is a shame that the authors do not explain more clearly (for instance, with a concrete illustration) what they mean precisely by those terms. The extraction of such features is of high interest but, unfortunately, it is not sufficiently detailed in this paper. Online Computing Reviews Service

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Published In

ICUIMC '09: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication

February 2009

704 pages

ISBN:9781605584058

DOI:10.1145/1516241

General Chairs:
Won Kim
Sungkyunkwan University, Korea
,
Hyung Jin Choi
Sungkyunkwan University, Korea
,
Dongho Won
SungKyunkwan University, Korea

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 15 February 2009

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ICUIMC '09

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SIGKDD

ICUIMC '09: The 3rd International Conference on Ubiquitous Information Management and Communication

January 15 - 16, 2009

Suwon, Korea

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Elagamy MStanier CSharp B(2018)Stock market random forest-text mining system mining critical indicators of stock market movements2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP)10.1109/ICNLSP.2018.8374370(1-8)Online publication date: Apr-2018
https://doi.org/10.1109/ICNLSP.2018.8374370
Elagamy MStanier CSharp B(2018)Text Mining Approach to Analyse Stock Market MovementThe International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018)10.1007/978-3-319-74690-6_65(661-670)Online publication date: 26-Jan-2018
https://doi.org/10.1007/978-3-319-74690-6_65

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Genetic-Based Type II Feature Extraction for Periocular Biometric Recognition: Less is More

AMRITA_CEN@FIRE-2014: Morpheme Extraction and Lemmatization for Tamil using Machine Learning

Two-Word Collocation Extraction Using Monolingual Word Alignment Method

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