Dr. Almuth Müller Profile

Dr. Almuth Müller

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 13 November 2024 Paper

Adaption of AI models for processing formal reports in the field of Joint ISR

Tobias Dorrn, Almuth Müller, Jennifer Sander

Proceedings Volume 13206, 132060A (2024) https://doi.org/10.1117/12.3031622

KEYWORDS: Data modeling, Intelligence systems, Education and training, Performance modeling, Artificial intelligence, Data processing, Semantics, Process modeling, Surveillance, Reconnaissance

Read Abstract +

In the area of Joint ISR (Joint Intelligence, Surveillance and Reconnaissance), it is important to have robust (semi- )automatic support for the identification and processing of text-based information products like formal reports. The representation of reports as text unifies contributions from heterogeneous information sources (e.g. delivered by various intelligence disciplines). Such text-based information products also often encapsulate dense information of high-quality. Therefore, the capability for machine processing to adequately integrate various pieces of information from different sources and display them to the user in a coherent and comprehensible manner is essential for maximizing the utility and accessibility of intelligence data/report information. Current AI models and methods from the field of Natural Language Processing (NLP) can make valuable contributions to the processing of text-based information in general, e.g. textsummarization, extraction of named entities or other important information-parts. They are widely used for social media applications. However, to adopt this capability for the military domain, they have to be adapted to the specific vocabulary of the Joint ISR domain and the grammatical structures. Especially challenging is the limited grammatical variance found within these text products, limiting the scarcity of available sample data suitable for training purposes even further. This publication examines the variations in training data for NLP methodologies that emerge when dealing with the Joint ISR domain and its reporting procedures. An approach is presented to capture entities within formalized texts using Named Entity Recognition (NER) and to illustrate how this approach can support the processing of textual information, especially formal reports, in the field of Joint ISR. The value of formal reporting is also emphasized for achieving syntactic and semantic interoperability within Joint ISR networks.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years