Stefan Schulz

Also published as: Stefan Schultz


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Localising the Clinical Terminology SNOMED CT by Semi-automated Creation of a German Interface Vocabulary
Stefan Schulz | Larissa Hammer | David Hashemian-Nik | Markus Kreuzthaler
Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)

Medical language exhibits great variations regarding users, institutions and language registers. With large parts of clinical documents in free text, NLP is playing a more and more important role in unlocking re-usable and interoperable meaning from medical records. This study describes the architectural principles and the evolution of a German interface vocabulary, combining machine translation with human annotation and rule-based term generation, yielding a resource with 7.7 million raw entries, each of which linked to the reference terminology SNOMED CT, an international standard with about 350 thousand concepts. The purpose is to offer a high coverage of medical jargon in order to optimise terminology grounding of clinical texts by text mining systems. The core resource is a manually curated table of English-to-German word and chunk translations, supported by a set of language generation rules. The work describes a workflow consisting the enrichment and modification of this table with human and machine efforts, manually enriched by grammarspecific tags. Top-down and bottom-up methods for terminology population used in parallel. The final interface terms are produced by a term generator, which creates one-to-many German variants per SNOMED CT English description. Filtering against a large collection of domain terminologies and corpora drastically reduces the size of the vocabulary in favour of more realistic terms or terms that can reasonably be expected to match clinical text passages within a text-mining pipeline. An evaluation was performed by a comparison between the current version of the German interface vocabulary and the English description table of the SNOMED CT International release. An exact term matching was performed with a small parallel corpus constituted by text snippets from different clinical documents. With overall low retrieval parameters (with F-values around 30%), the performance of the German language scenario reaches 80 – 90% of the English one. Interestingly, annotations are slightly better with machine-translated (German – English) texts, using the International SNOMED CT resource only.


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Unsupervised Abbreviation Detection in Clinical Narratives
Markus Kreuzthaler | Michel Oleynik | Alexander Avian | Stefan Schulz
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)

Clinical narratives in electronic health record systems are a rich resource of patient-based information. They constitute an ongoing challenge for natural language processing, due to their high compactness and abundance of short forms. German medical texts exhibit numerous ad-hoc abbreviations that terminate with a period character. The disambiguation of period characters is therefore an important task for sentence and abbreviation detection. This task is addressed by a combination of co-occurrence information of word types with trailing period characters, a large domain dictionary, and a simple rule engine, thus merging statistical and dictionary-based disambiguation strategies. An F-measure of 0.95 could be reached by using the unsupervised approach presented in this paper. The results are promising for a domain-independent abbreviation detection strategy, because our approach avoids retraining of models or use case specific feature engineering efforts required for supervised machine learning approaches.


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Disambiguation of Period Characters in Clinical Narratives
Markus Kreuzthaler | Stefan Schulz
Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)


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Creation and use of Language Resources in a Question-Answering eHealth System
Ulrich Andersen | Anna Braasch | Lina Henriksen | Csaba Huszka | Anders Johannsen | Lars Kayser | Bente Maegaard | Ole Norgaard | Stefan Schulz | Jürgen Wedekind
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

ESICT (Experience-oriented Sharing of health knowledge via Information and Communication Technology) is an ongoing research project funded by the Danish Council for Strategic Research. It aims at developing a health/disease related information system based on information technology, language technology, and formalized medical knowledge. The formalized medical knowledge consists partly of the terminology database SNOMED CT and partly of authorized medical texts on the domain. The system will allow users to ask questions in Danish and will provide natural language answers. Currently, the project is pursuing three basically different methods for question answering, and they are all described to some extent in this paper. A system prototype will handle questions related to diabetes and heart diseases. This paper concentrates on the methods employed for question answering and the language resources that are utilized. Some resources were existing, such as SNOMED CT, others, such as a corpus of sample questions, have had to be created or constructed.


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Language Specific and Topic Focused Web Crawling
Olena Medelyan | Stefan Schulz | Jan Paetzold | Michael Poprat | Kornél Markó
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We describe an experiment on collecting large language and topic specific corpora automatically by using a focused Web crawler. Our crawler combines efficient crawling techniques with a common text classification tool. Given a sample corpus of medical documents, we automatically extract query phrases and then acquire seed URLs with a standard search engine. Starting from these seed URLs, the crawler builds a new large collection consisting only of documents that satisfy both the language and the topic model. The manual analysis of acquired English and German medicine corpora reveals the high accuracy of the crawler. However, there are significant differences between both languages.

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Semantic Atomicity and Multilinguality in the Medical Domain: Design Considerations for the MorphoSaurus Subword Lexicon
Stefan Schulz | Kornél Markó | Philipp Daumke | Udo Hahn | Susanne Hanser | Percy Nohama | Roosewelt Leite de Andrade | Edson Pacheco | Martin Romacker
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We present the lexico-semantic foundations underlying a multilingual lexicon the entries of which are constituted by so-called subwords. These subwords reflect semantic atomicity constraints in the medical domain which diverge from canonical lexicological understanding in NLP. We focus here on criteria to identify and delimit reasonable subword units, to group them into functionally adequate synonymy classes and relate them by two types of lexical relations. The lexicon we implemented on the basis of these considerations forms the lexical backbone for MorphoSaurus, a cross-language document retrieval engine for the medical domain.


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Subword Clusters as Light-Weight Interlingua for Multilingual Document Retrieval
Udo Hahn | Kornel Marko | Stefan Schulz
Proceedings of Machine Translation Summit X: Papers

We introduce a light-weight interlingua for a cross-language document retrieval system in the medical domain. It is composed of equivalence classes of semantically primitive, language-specific subwords which are clustered by interlingual and intralingual synonymy. Each subword cluster represents a basic conceptual entity of the language-independent interlingua. Documents, as well as queries, are mapped to this interlingua level on which retrieval operations are performed. Evaluation experiments reveal that this interlingua-based retrieval model outperforms a direct translation approach.


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Cognate Mapping - A Heuristic Strategy for the Semi-Supervised Acquisition of a Spanish Lexicon from a Portuguese Seed Lexicon
Stefan Schulz | Kornel Markó | Eduardo Sbrissia | Percy Nohama | Udo Hahn
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics


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Towards Very Large Ontologies for Medical Language Processing
Udo Hahn | Stefan Schulz
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Biomedical text retrieval in languages with a complex morphology
Stefan Schultz | Martin Honeck | Udo Hahn
Proceedings of the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain