A fast associative mining system based on search engine and concept graph for large-scale financial report texts

Kun Qian; Sachio Hirokawa; Kenji Ejima; Xiaoping Du

doi:10.1109/ICIFE.2010.5609447

A fast associative mining system based on search engine and concept graph for large-scale financial report texts

Kun Qian, Sachio Hirokawa, Kenji Ejima, Xiaoping Du

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

Association mining is widely used in pattern discovery. For large scale financial textual data analysis, however, association mining is relatively less applied due to low efficiency in text manipulation. This paper presents a fast finance textual mining system, based on search engine and concept graph, for large scale financial textual association mining and visualization. Through the experiments on ten years' financial reports of 6,049 companies from NASDAQ and NYSE from 1999 to 2008, it testified that this system could rapidly extracting the characteristic words among millions of texts and visualizing them by concept graph in seconds.

Original language	English
Title of host publication	Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010
Pages	675-679
Number of pages	5
DOIs	https://doi.org/10.1109/ICIFE.2010.5609447
Publication status	Published - Dec 23 2010
Event	2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010 - Chongqing, China Duration: Sept 17 2010 → Sept 19 2010

Publication series

Name	Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010

Other

Other	2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010
Country/Territory	China
City	Chongqing
Period	9/17/10 → 9/19/10

All Science Journal Classification (ASJC) codes

Accounting
Finance

Access to Document

10.1109/ICIFE.2010.5609447

Cite this

Qian, K., Hirokawa, S., Ejima, K., & Du, X. (2010). A fast associative mining system based on search engine and concept graph for large-scale financial report texts. In Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010 (pp. 675-679). [5609447] (Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010). https://doi.org/10.1109/ICIFE.2010.5609447

A fast associative mining system based on search engine and concept graph for large-scale financial report texts. / Qian, Kun; Hirokawa, Sachio; Ejima, Kenji et al.
Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010. 2010. p. 675-679 5609447 (Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Qian, K, Hirokawa, S, Ejima, K & Du, X 2010, A fast associative mining system based on search engine and concept graph for large-scale financial report texts. in Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010., 5609447, Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010, pp. 675-679, 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010, Chongqing, China, 9/17/10. https://doi.org/10.1109/ICIFE.2010.5609447

Qian K, Hirokawa S, Ejima K, Du X. A fast associative mining system based on search engine and concept graph for large-scale financial report texts. In Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010. 2010. p. 675-679. 5609447. (Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010). doi: 10.1109/ICIFE.2010.5609447

Qian, Kun ; Hirokawa, Sachio ; Ejima, Kenji et al. / A fast associative mining system based on search engine and concept graph for large-scale financial report texts. Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010. 2010. pp. 675-679 (Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010).

@inproceedings{aa2bd88283f44143a9ed4a36929dc3ae,

title = "A fast associative mining system based on search engine and concept graph for large-scale financial report texts",

abstract = "Association mining is widely used in pattern discovery. For large scale financial textual data analysis, however, association mining is relatively less applied due to low efficiency in text manipulation. This paper presents a fast finance textual mining system, based on search engine and concept graph, for large scale financial textual association mining and visualization. Through the experiments on ten years' financial reports of 6,049 companies from NASDAQ and NYSE from 1999 to 2008, it testified that this system could rapidly extracting the characteristic words among millions of texts and visualizing them by concept graph in seconds.",

author = "Kun Qian and Sachio Hirokawa and Kenji Ejima and Xiaoping Du",

year = "2010",

month = dec,

day = "23",

doi = "10.1109/ICIFE.2010.5609447",

language = "English",

isbn = "9781424469253",

series = "Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010",

pages = "675--679",

booktitle = "Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010",

note = "2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010 ; Conference date: 17-09-2010 Through 19-09-2010",

}

TY - GEN

T1 - A fast associative mining system based on search engine and concept graph for large-scale financial report texts

AU - Qian, Kun

AU - Hirokawa, Sachio

AU - Ejima, Kenji

AU - Du, Xiaoping

PY - 2010/12/23

Y1 - 2010/12/23

N2 - Association mining is widely used in pattern discovery. For large scale financial textual data analysis, however, association mining is relatively less applied due to low efficiency in text manipulation. This paper presents a fast finance textual mining system, based on search engine and concept graph, for large scale financial textual association mining and visualization. Through the experiments on ten years' financial reports of 6,049 companies from NASDAQ and NYSE from 1999 to 2008, it testified that this system could rapidly extracting the characteristic words among millions of texts and visualizing them by concept graph in seconds.

AB - Association mining is widely used in pattern discovery. For large scale financial textual data analysis, however, association mining is relatively less applied due to low efficiency in text manipulation. This paper presents a fast finance textual mining system, based on search engine and concept graph, for large scale financial textual association mining and visualization. Through the experiments on ten years' financial reports of 6,049 companies from NASDAQ and NYSE from 1999 to 2008, it testified that this system could rapidly extracting the characteristic words among millions of texts and visualizing them by concept graph in seconds.

UR - http://www.scopus.com/inward/record.url?scp=78650268520&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78650268520&partnerID=8YFLogxK

U2 - 10.1109/ICIFE.2010.5609447

DO - 10.1109/ICIFE.2010.5609447

M3 - Conference contribution

AN - SCOPUS:78650268520

SN - 9781424469253

T3 - Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010

SP - 675

EP - 679

BT - Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010

T2 - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010

Y2 - 17 September 2010 through 19 September 2010

ER -

A fast associative mining system based on search engine and concept graph for large-scale financial report texts

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this