Mr. Tobias Dorrn Profile

Tobias Dorrn

at Fraunhofer IOSB

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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

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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.

Proceedings Article | 28 October 2022 Presentation + Paper

Dynamic-automatic pipelines for finding topic-specific information clusters using NLP methods in connection with a model-driven approach

Tobias Dorrn, Achim Kuwertz

Proceedings Volume 12276, 1227602 (2022) https://doi.org/10.1117/12.2648385

KEYWORDS: Data modeling, Systems modeling, Data processing, Associative arrays, Roads, Visualization, Solid modeling, Computer security

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Finding and extracting topic-specific information from free-text sources is an important task for classifying and distinguishing content of information systems. Such a compression process of information, in which non-relevant text parts can also be ignored, is also advantageous with regard to the further machine processing and evaluation of topic-specific documents. State-of-the-art approaches normally use well-trained modern Natural Language Processing (NLP) methods to solve such tasks. However, use cases can arise where no suitable training data sets are available to adequately prepare or fine-tune the NLP methods used. In this paper, we want to detail a model-driven approach, applying an XML data model to an application-specific scenario, combining different NLP methods into a dynamic automated NLP pipeline. The goal of this pipeline is the automatic extraction of specific information (related to certain domains or topics) from text documents allowing a structured further processing of this information. Specifically, a scenario is considered where such information has to be aligned to a given information model, defining e.g. the terms relevant for the further processing. The solution approaches described here deal with a scenario in which information clusters on a specific topic can be obtained from a given data set, even without domain-specific model training. The basis is the use of a dynamic (i.e., using different NLP methods and models) and fully automatic (i.e., using different topics at the same time) pipeline architecture combined with an XML data model. The presented approach details and extends our earlier work and gives new qualitative and first quantitative results.

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