KIM, Sanghee ;WALLACE, Ken ;AHMED, Saeema . Improving Information Extraction using a Probability-Based Approach. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 53, n.7-8, p. 429-441, november 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/improving-information-extraction-using-a-probability-based-approach/>. Date accessed: 20 apr. 2024. doi:http://dx.doi.org/.
Kim, S., Wallace, K., & Ahmed, S. (2007). Improving Information Extraction using a Probability-Based Approach. Strojniški vestnik - Journal of Mechanical Engineering, 53(7-8), 429-441. doi:http://dx.doi.org/
@article{.,
author = {Sanghee Kim and Ken Wallace and Saeema Ahmed},
title = {Improving Information Extraction using a Probability-Based Approach},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {53},
number = {7-8},
year = {2007},
keywords = {information searches; name entity identification; natural language processing; taxonomy; probability method; },
abstract = {Information plays a crucial role during the entire life-cycle of a product. It has been shown that engineers frequently consult colleagues to obtain the information they require to solve problems. However, thee industrial world is now more transient and key personnel move to other companies or retire. It is becoming essential to retrieve vital information from archived product documents, if it is available. There is, therefore, great interest in ways of extracting relevant and sharable information from documents. A key-word-based research is commonly used, but studies have shown that these searches often prove unsuccessful. Searches can be improved if domain entities of interest e.g., 'gas turbine', are referring to the entities using various different ways of expressing them. It would be helpful to compile a full list of entities associatedwith the relevant types before identifying them in texts. However, due to the various ways of reffering entities in the texts, manually defined identification rules tend to produce high precision, a learning approach that makes pre-defined variations, looks promising. This paper presnets the results of developing such a probability-based entity-identification approach. Tests show that the porposed approach achieves imporved recall, i.e, from 53% to 80% with comparable precision.},
issn = {0039-2480}, pages = {429-441}, doi = {},
url = {https://www.sv-jme.eu/sl/article/improving-information-extraction-using-a-probability-based-approach/}
}
Kim, S.,Wallace, K.,Ahmed, S. 2007 November 53. Improving Information Extraction using a Probability-Based Approach. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 53:7-8
%A Kim, Sanghee %A Wallace, Ken %A Ahmed, Saeema %D 2007 %T Improving Information Extraction using a Probability-Based Approach %B 2007 %9 information searches; name entity identification; natural language processing; taxonomy; probability method; %! Improving Information Extraction using a Probability-Based Approach %K information searches; name entity identification; natural language processing; taxonomy; probability method; %X Information plays a crucial role during the entire life-cycle of a product. It has been shown that engineers frequently consult colleagues to obtain the information they require to solve problems. However, thee industrial world is now more transient and key personnel move to other companies or retire. It is becoming essential to retrieve vital information from archived product documents, if it is available. There is, therefore, great interest in ways of extracting relevant and sharable information from documents. A key-word-based research is commonly used, but studies have shown that these searches often prove unsuccessful. Searches can be improved if domain entities of interest e.g., 'gas turbine', are referring to the entities using various different ways of expressing them. It would be helpful to compile a full list of entities associatedwith the relevant types before identifying them in texts. However, due to the various ways of reffering entities in the texts, manually defined identification rules tend to produce high precision, a learning approach that makes pre-defined variations, looks promising. This paper presnets the results of developing such a probability-based entity-identification approach. Tests show that the porposed approach achieves imporved recall, i.e, from 53% to 80% with comparable precision. %U https://www.sv-jme.eu/sl/article/improving-information-extraction-using-a-probability-based-approach/ %0 Journal Article %R %& 429 %P 13 %J Strojniški vestnik - Journal of Mechanical Engineering %V 53 %N 7-8 %@ 0039-2480 %8 2017-11-03 %7 2017-11-03
Kim, Sanghee, Ken Wallace, & Saeema Ahmed. "Improving Information Extraction using a Probability-Based Approach." Strojniški vestnik - Journal of Mechanical Engineering [Online], 53.7-8 (2007): 429-441. Web. 20 Apr. 2024
TY - JOUR AU - Kim, Sanghee AU - Wallace, Ken AU - Ahmed, Saeema PY - 2007 TI - Improving Information Extraction using a Probability-Based Approach JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - information searches; name entity identification; natural language processing; taxonomy; probability method; N2 - Information plays a crucial role during the entire life-cycle of a product. It has been shown that engineers frequently consult colleagues to obtain the information they require to solve problems. However, thee industrial world is now more transient and key personnel move to other companies or retire. It is becoming essential to retrieve vital information from archived product documents, if it is available. There is, therefore, great interest in ways of extracting relevant and sharable information from documents. A key-word-based research is commonly used, but studies have shown that these searches often prove unsuccessful. Searches can be improved if domain entities of interest e.g., 'gas turbine', are referring to the entities using various different ways of expressing them. It would be helpful to compile a full list of entities associatedwith the relevant types before identifying them in texts. However, due to the various ways of reffering entities in the texts, manually defined identification rules tend to produce high precision, a learning approach that makes pre-defined variations, looks promising. This paper presnets the results of developing such a probability-based entity-identification approach. Tests show that the porposed approach achieves imporved recall, i.e, from 53% to 80% with comparable precision. UR - https://www.sv-jme.eu/sl/article/improving-information-extraction-using-a-probability-based-approach/
@article{{}{.},
author = {Kim, S., Wallace, K., Ahmed, S.},
title = {Improving Information Extraction using a Probability-Based Approach},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {53},
number = {7-8},
year = {2007},
doi = {},
url = {https://www.sv-jme.eu/sl/article/improving-information-extraction-using-a-probability-based-approach/}
}
TY - JOUR AU - Kim, Sanghee AU - Wallace, Ken AU - Ahmed, Saeema PY - 2017/11/03 TI - Improving Information Extraction using a Probability-Based Approach JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 53, No 7-8 (2007): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - information searches, name entity identification, natural language processing, taxonomy, probability method, N2 - Information plays a crucial role during the entire life-cycle of a product. It has been shown that engineers frequently consult colleagues to obtain the information they require to solve problems. However, thee industrial world is now more transient and key personnel move to other companies or retire. It is becoming essential to retrieve vital information from archived product documents, if it is available. There is, therefore, great interest in ways of extracting relevant and sharable information from documents. A key-word-based research is commonly used, but studies have shown that these searches often prove unsuccessful. Searches can be improved if domain entities of interest e.g., 'gas turbine', are referring to the entities using various different ways of expressing them. It would be helpful to compile a full list of entities associatedwith the relevant types before identifying them in texts. However, due to the various ways of reffering entities in the texts, manually defined identification rules tend to produce high precision, a learning approach that makes pre-defined variations, looks promising. This paper presnets the results of developing such a probability-based entity-identification approach. Tests show that the porposed approach achieves imporved recall, i.e, from 53% to 80% with comparable precision. UR - https://www.sv-jme.eu/sl/article/improving-information-extraction-using-a-probability-based-approach/
Kim, Sanghee, Wallace, Ken, AND Ahmed, Saeema. "Improving Information Extraction using a Probability-Based Approach" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 53 Number 7-8 (03 November 2017)
Strojniški vestnik - Journal of Mechanical Engineering 53(2007)7-8, 429-441
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Information plays a crucial role during the entire life-cycle of a product. It has been shown that engineers frequently consult colleagues to obtain the information they require to solve problems. However, thee industrial world is now more transient and key personnel move to other companies or retire. It is becoming essential to retrieve vital information from archived product documents, if it is available. There is, therefore, great interest in ways of extracting relevant and sharable information from documents. A key-word-based research is commonly used, but studies have shown that these searches often prove unsuccessful. Searches can be improved if domain entities of interest e.g., 'gas turbine', are referring to the entities using various different ways of expressing them. It would be helpful to compile a full list of entities associatedwith the relevant types before identifying them in texts. However, due to the various ways of reffering entities in the texts, manually defined identification rules tend to produce high precision, a learning approach that makes pre-defined variations, looks promising. This paper presnets the results of developing such a probability-based entity-identification approach. Tests show that the porposed approach achieves imporved recall, i.e, from 53% to 80% with comparable precision.