Extracting Meaningful Entities from Human-generated Tactical Reports

Guo, Jinhong K.; Brackle, David Van; Lofaso, Nicolas; Hofmann, Martin

doi:10.1016/j.procs.2015.09.153

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Automated text analysis using methods and tools from the field of natural language processing has been proposed as a way to off-load some of the selective and interpretative work from human intelligence analysts. With regard to more closely related government and military intelligence tasks, Guo et al [23] did work on entity extraction from human-generated tactical reports to support intelligence analysis. They extracted entities such as organizations, locations, persons, etc., with promising results.…”

Section: Related Workmentioning

confidence: 99%

Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study

Varga

Brynielsson

Horndahl

et al. 2020

Lecture Notes in Social Networks

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With the availability of an abundance of data through the Internet, the premises to solve some intelligence analysis tasks have changed for the better. The study presented herein sets out to examine whether and how a data-driven approach can contribute to solve intelligence tasks. During a full day observational study, an ordinary military intelligence unit was divided into two uniform teams. Each team was independently asked to solve the same realistic intelligence analysis task. Both teams were allowed to use their ordinary set of tools, but in addition one team was also given access to a novel text analysis prototype tool specifically designed to support data-driven intelligence analysis of social media data. The results, obtained from the case study with a high ecological validity, suggest that the prototype tool provided valuable insights by bringing forth information from a more diverse set of sources, specifically from private citizens that would not have been easily discovered otherwise. Also, regardless of its objective contribution, the capabilities and the usage of the tool were embraced and subjectively perceived as useful by all involved analysts.

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Section: Related Workmentioning

confidence: 99%

Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study

Varga

Brynielsson

Horndahl

et al. 2020

Lecture Notes in Social Networks

View full text Add to dashboard Cite

show abstract

“…The first method constructs the rules by linguistic experts manually and assigns weights to each rule, and then determines the type according to the conformity of entities and rules. Based on the rules and a dictionary, Guo et al [7] adopted a custom semantic pattern to extract entities in tactical reports. Although the recognition accuracy was high, its compilation process was time-consuming.…”

Section: Related Workmentioning

confidence: 99%

Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military

Liao

Pei³

et al. 2019

Future Internet

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Military named entity recognition (MNER) is one of the key technologies in military information extraction. Traditional methods for the MNER task rely on cumbersome feature engineering and specialized domain knowledge. In order to solve this problem, we propose a method employing a bidirectional long short-term memory (BiLSTM) neural network with a self-attention mechanism to identify the military entities automatically. We obtain distributed vector representations of the military corpus by unsupervised learning and the BiLSTM model combined with the self-attention mechanism is adopted to capture contextual information fully carried by the character vector sequence. The experimental results show that the self-attention mechanism can improve effectively the performance of MNER task. The F-score of the military documents and network military texts identification was 90.15% and 89.34%, respectively, which was better than other models.

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“…In a database, any two of the entities probability similarity value is same, then the two entities are representing same. Guo et al, [12] proposed set of syntactic patterns for extracting effective entities from reviews. Zafarani et al, [13] suggested an effective approach as link based user identification, which is used to map two social networks.…”

Section: Related Workmentioning

confidence: 99%

An empirical semi-supervised machine learning approach on extracting and ranking document level multi-word product names using improved C-value approach

Sivashankari

Valarmathi

2016

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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In recent years, the volume of data submissions (E-Commerce data) in online on products, service, and organizations is increasing exponentially. This online data is abundantly unstructured; extracting knowledge from that huge volume of data is a non-trivial task. In recent years, extracting product names become a very popular approach and also one of the important methods in sentiment analysis. This product name extraction is very useful in E-commerce, because it helps in identifying people interest on products, generation of reviews' metadata and identification of product attributes, etc. The existing approaches in product name extraction are capable of extracting single word product names. However, the product names can be a sequence of words, which is also called multi-word product names that cannot be obtained automatically by the existing methods. In this paper, a combined approach of semi-supervised machine learning and improved C-value approach is proposed to discover the multi-word product names, ranking those product names and identifying a dominant product in review documents.

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Extracting Meaningful Entities from Human-generated Tactical Reports

Cited by 5 publications

References 11 publications

Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study

Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study

Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military

An empirical semi-supervised machine learning approach on extracting and ranking document level multi-word product names using improved C-value approach

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