research-article

TEST: A Terminology Extraction System for Technology Related Terms

Authors:

Murhaf Hossari,

Soumyabrata Dev,

John D. KelleherAuthors Info & Claims

ICCAE 2019: Proceedings of the 2019 11th International Conference on Computer and Automation Engineering

Pages 78 - 81

https://doi.org/10.1145/3313991.3314006

Published: 23 February 2019 Publication History

Get Access

Abstract

Tracking developments in the highly dynamic data-technology landscape are vital to keeping up with novel technologies and tools, in the various areas of Artificial Intelligence (AI). However, It is difficult to keep track of all the relevant technology keywords. In this paper, we propose a novel system that addresses this problem. This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies. The extracted new terms can be logged as new AI technologies as they are found on-the-fly in the web. It can be subsequently classified into the relevant semantic labels and AI domains. Our proposed tool is based on a two-stage cascading model--the first stage classifies if the sentence contains a technology term or not; and the second stage identifies the technology keyword in the sentence. We obtain a competitive accuracy for both tasks of sentence classification and text identification.

References

[1]

C. C. Aggarwal and C. Zhai. 2012. A survey of text classification algorithms. In Mining text data. Springer, 163--222.

Google Scholar

[2]

S. Chakrabarti, B. Dom, R. Agrawal, and P. Raghavan. 1997. Using taxonomy, discriminants, and signatures for navigating in text databases. In VLDB, Vol. 97. 446--455.

Digital Library

Google Scholar

[3]

J. R. Finkel, T. Grenager, and C. Manning. 2005. Incorporating non-local information into information extraction systems by Gibbs sampling. In Proceedings of the 43rd annual meeting on association for computational linguistics. Association for Computational Linguistics, 363--370.

Digital Library

Google Scholar

[4]

M. Hossari, S. Dev, M. Nicholson, K. McCabe, A. Nautiyal, C. Conran, J. Tang, X. Wei, and F. Pitie. 2018. ADNet: A Deep Network for Detecting Adverts. In Proc. Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2018).

Google Scholar

[5]

A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov. 2016. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016).

Google Scholar

[6]

J. D. Kelleher and B. Tierney. 2018. Data Science. The MIT Press.

Digital Library

Google Scholar

[7]

A. Nautiyal, K. McCabe, M. Hossari, S. Dev, M. Nicholson, C. Conran, D. McKibben, J. Tang, X. Wei, and F. Pitié. 2018. An Advert Creation System for Next-Gen Publicity. In Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD).

Google Scholar

[8]

M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. 1998. A Bayesian approach to filtering junk e-mail. In Learning for Text Categorization: Papers from the 1998 workshop, Vol. 62. Madison, Wisconsin, 98--105.

Google Scholar

Cited By

View all

Pham HNguyen MHa HLe NJo J(2024)Towards Comprehensive Innovation Landscape: Technology Retrieval Meets Large Language ModelsDatabases Theory and Applications10.1007/978-981-96-1242-0_7(85-98)Online publication date: 13-Dec-2024
https://doi.org/10.1007/978-981-96-1242-0_7
Giordano VChiarello FMelluso NFantoni GBonaccorsi A(2023)Text and Dynamic Network Analysis for Measuring Technological Convergence: A Case Study on Defense Patent DataIEEE Transactions on Engineering Management10.1109/TEM.2021.307823170:4(1490-1503)Online publication date: Apr-2023
https://doi.org/10.1109/TEM.2021.3078231
Duong CDavid DDolamic LMermoud ALenders VAberer K(2023)From scattered sources to comprehensive technology landscape : A recommendation-based retrieval approachWorld Patent Information10.1016/j.wpi.2023.10219873(102198)Online publication date: Jun-2023
https://doi.org/10.1016/j.wpi.2023.102198
Show More Cited By

Index Terms

TEST: A Terminology Extraction System for Technology Related Terms

Recommendations

Co-clustering sentences and terms for multi-document summarization
CICLing'11: Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II

Two issues are crucial to multi-document summarization: diversity and redundancy. Content within some topically-related articles are usually redundant while the topic is delivered from diverse perspectives. This paper presents a co-clustering based ...
Discovering "title-like" terms

This paper examines the feasibility of discovering "title-like" terms using a decision tree classifier from the document. The premise of discovering title-like terms is that title terms and title-like terms should behave similarly in the document. This ...
Comparison of feature selection methods for sentiment analysis
AI'10: Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence

Sentiment analysis is a sub-field of Natural Language Processing and involves automatically classifying input text according to the sentiment expressed in it Sentiment analysis is similar to topical text classification but has a significant contextual ...

Comments

Information & Contributors

Information

Published In

ICCAE 2019: Proceedings of the 2019 11th International Conference on Computer and Automation Engineering

February 2019

160 pages

ISBN:9781450362870

DOI:10.1145/3313991

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]

In-Cooperation

The University of Western Australia, Department of Electronic Engineering, University of Western Australia
University of Melbourne: University of Melbourne
Macquarie University-Sydney

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 February 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICCAE 2019

ICCAE 2019: 2019 11th International Conference on Computer and Automation Engineering

February 23 - 25, 2019

WN, Perth, Australia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
111
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)1

Reflects downloads up to 15 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Pham HNguyen MHa HLe NJo J(2024)Towards Comprehensive Innovation Landscape: Technology Retrieval Meets Large Language ModelsDatabases Theory and Applications10.1007/978-981-96-1242-0_7(85-98)Online publication date: 13-Dec-2024
https://doi.org/10.1007/978-981-96-1242-0_7
Giordano VChiarello FMelluso NFantoni GBonaccorsi A(2023)Text and Dynamic Network Analysis for Measuring Technological Convergence: A Case Study on Defense Patent DataIEEE Transactions on Engineering Management10.1109/TEM.2021.307823170:4(1490-1503)Online publication date: Apr-2023
https://doi.org/10.1109/TEM.2021.3078231
Duong CDavid DDolamic LMermoud ALenders VAberer K(2023)From scattered sources to comprehensive technology landscape : A recommendation-based retrieval approachWorld Patent Information10.1016/j.wpi.2023.10219873(102198)Online publication date: Jun-2023
https://doi.org/10.1016/j.wpi.2023.102198
Berjon PSharma RNag ADev S(2022)Frequency-Centroid Features Forword Recognition of Non-Native English Speakers2022 33rd Irish Signals and Systems Conference (ISSC)10.1109/ISSC55427.2022.9826202(1-6)Online publication date: 9-Jun-2022
https://doi.org/10.1109/ISSC55427.2022.9826202
Bruches EBatura T(2021)Method for Automatic Term Extraction from Scientific Articles Based on Weak SupervisionVestnik NSU. Series: Information Technologies10.25205/1818-7900-2021-19-2-5-1619:2(5-16)Online publication date: 20-Jul-2021
https://doi.org/10.25205/1818-7900-2021-19-2-5-16
Berjon PNag ADev S(2021)Analysis of French phonetic idiosyncrasies for accent recognitionSoft Computing Letters10.1016/j.socl.2021.100018(100018)Online publication date: Sep-2021
https://doi.org/10.1016/j.socl.2021.100018

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Co-clustering sentences and terms for multi-document summarization

Discovering "title-like" terms

Comparison of feature selection methods for sentiment analysis

Comments

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Co-clustering sentences and terms for multi-document summarization

Discovering "title-like" terms

Comparison of feature selection methods for sentiment analysis

Comments

Information

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations