Improving term extraction by utilizing user annotations

2013 ◽  
Author(s):  
Jozef Harinek ◽  
Marián Šimko
Keyword(s):  
Term Extraction

Term extraction and management based on event templates

Terminology ◽  
2014 ◽  
Vol 20 (2) ◽  
pp. 151-170 ◽  
Author(s):  
Katia Peruzzo
Keyword(s):  
European Union ◽  
Knowledge Base ◽  
Legal System ◽  
Classification Criteria ◽  
The European Union ◽  
Additional Information ◽  
Term Extraction ◽  
Automatic Term Extraction ◽  
Multi Level ◽  
Victims Of Crime

The paper examines the possible usage of event templates derived from Frame-Based Terminology (Faber et al. 2005, 2006, 2007) as an aid to the extraction and management of legal terminology embedded in the multi-level legal system of the European Union. The method proposed here, which combines semi-automatic term extraction and a simplified event template containing six categories, is applied to an English corpus of EU texts focusing on victims of crime and their rights. Such a combination allows for the extraction of category-relevant terminological units and additional information, which can then be used for populating a terminological knowledge base organised on the basis of the same event template, but which also employs additional classification criteria to account for the multidimensionality encountered in the corpus.

“This Mine is for the Entire Casamance Coastline”: The Politics of Scale and the Future of the Extractive Frontier in Casamance, Senegal

2021 ◽  
Vol 64 (3) ◽  
pp. 628-652
Author(s):  
Ashley Fent
Keyword(s):  
Local Level ◽  
Small Scale ◽  
Limited Extent ◽  
Politics Of Scale ◽  
Space And Time ◽  
Term Extraction ◽  
Mining Investment ◽  
The Future

AbstractAs evidenced by the widespread controversy surrounding an otherwise small-scale mining investment pending in Casamance, Senegal, uncertainty shapes the extension of the extractive frontier. Fent argues that amid this uncertainty, different actors are able to politicize or depoliticize extractive investments through the work of scaling. Opponents cast the project as part of larger-scale, longer-term extraction, linking it with regional narratives. By contrast, state and corporate actors depoliticized the mine by emphasizing its limited extent and downscaling conflict to the local level. This demonstrates the conflictual processes through which extractive frontiers are realized—and resisted—through both space and time.

scholarly journals A Novel Method for Arabic Multi-Word Term Extraction

2014 ◽  
Vol 6 (3) ◽  
pp. 53-67 ◽  
Author(s):  
Hadni Meryem ◽  
Said Alaoui Ouatik ◽  
Abdelmonaime Lachkar
Keyword(s):  
Term Extraction ◽  
Novel Method

scholarly journals Aspect Term Extraction for Aspect Based Opinion Mining

2019 ◽  
Vol 8 (11) ◽  
pp. 2228-2233
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Random Fields ◽  
Opinion Mining ◽  
Nearest Neighbor ◽  
Conditional Random Fields ◽  
International Workshop ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Term Extraction

Opinion Mining (OM) is also called as Sentiment Analysis (SA). Aspect Based Opinion Mining (ABOM) is also called as Aspect Based Sentiment Analysis (ABSA). In this paper, three new features are proposed to extract the aspect term for Aspect Based Sentiment Analysis (ABSA). The influence of the proposed features is evaluated on five classifiers namely Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Conditional Random Fields (CRF). The proposed features are evaluated on the Two datasets on Restaurant and Laptop domains available in International Workshop on Semantic Evaluation 2014 i.e. SemEval 2014. The influence of proposed features is evaluated using Precision, Recall and F1 measures. The proposed features are highly influencing for aspect term extraction on classifiers. The performance of SVM and CRF classifiers with proposed features is more influencing for aspect term extraction compared with NB, DT and KNN classifiers.

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