@inproceedings{dangendorf-etal-2026-extracting,
title = "Extracting Multi-Word Expressions Representing Technical Terms and Proper Nouns in Log Messages",
author = {Dangendorf, Kilian and
H{\"a}nsel, Sven-Ove and
Rosendahl, Jannik and
Heine, Felix and
Kleiner, Carsten and
Wartena, Christian},
editor = {Ojha, Atul Kr. and
Mititelu, Verginica Barbu and
Constant, Mathieu and
Stoyanova, Ivelina and
Do{\u{g}}ru{\"o}z, A. Seza and
Rademaker, Alexandre},
booktitle = "Proceedings of the 22nd Workshop on Multiword Expressions ({MWE} 2026)",
month = mar,
year = "2026",
address = "Rabat, Marocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mwe-1.7/",
pages = "61--65",
ISBN = "979-8-89176-363-0",
abstract = "IT-systems generate log messages containing important information about the system{'}s health. To gather information about system entities, we extract technical terms and proper nouns as multi-word expressions (MWEs) from a wide range of log messages from 16 different real systems. We apply Gries' information-theoretic approach which iteratively calculates the best MWE candidates using an eight-dimensional ranking method. These candidates are evaluated in an annotation study, achieving a precision of 66 {\%}. This value is significantly higher than evaluations on general-purpose texts, demonstrating the higher occurrence of compound technical terms and proper nouns in log messages. The MWEs found can be used to reduce the number of nodes in a system behavior graph while increasing the information density of the nodes."
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<abstract>IT-systems generate log messages containing important information about the system’s health. To gather information about system entities, we extract technical terms and proper nouns as multi-word expressions (MWEs) from a wide range of log messages from 16 different real systems. We apply Gries’ information-theoretic approach which iteratively calculates the best MWE candidates using an eight-dimensional ranking method. These candidates are evaluated in an annotation study, achieving a precision of 66 %. This value is significantly higher than evaluations on general-purpose texts, demonstrating the higher occurrence of compound technical terms and proper nouns in log messages. The MWEs found can be used to reduce the number of nodes in a system behavior graph while increasing the information density of the nodes.</abstract>
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%0 Conference Proceedings
%T Extracting Multi-Word Expressions Representing Technical Terms and Proper Nouns in Log Messages
%A Dangendorf, Kilian
%A Hänsel, Sven-Ove
%A Rosendahl, Jannik
%A Heine, Felix
%A Kleiner, Carsten
%A Wartena, Christian
%Y Ojha, Atul Kr.
%Y Mititelu, Verginica Barbu
%Y Constant, Mathieu
%Y Stoyanova, Ivelina
%Y Doğruöz, A. Seza
%Y Rademaker, Alexandre
%S Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-363-0
%F dangendorf-etal-2026-extracting
%X IT-systems generate log messages containing important information about the system’s health. To gather information about system entities, we extract technical terms and proper nouns as multi-word expressions (MWEs) from a wide range of log messages from 16 different real systems. We apply Gries’ information-theoretic approach which iteratively calculates the best MWE candidates using an eight-dimensional ranking method. These candidates are evaluated in an annotation study, achieving a precision of 66 %. This value is significantly higher than evaluations on general-purpose texts, demonstrating the higher occurrence of compound technical terms and proper nouns in log messages. The MWEs found can be used to reduce the number of nodes in a system behavior graph while increasing the information density of the nodes.
%U https://aclanthology.org/2026.mwe-1.7/
%P 61-65
Markdown (Informal)
[Extracting Multi-Word Expressions Representing Technical Terms and Proper Nouns in Log Messages](https://aclanthology.org/2026.mwe-1.7/) (Dangendorf et al., MWE 2026)
ACL