Mark-Christoph Müller

Also published as: Mark-Christoph Mueller


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The ARRAU 3.0 Corpus
Massimo Poesio | Maris Camilleri | Paloma Carretero Garcia | Juntao Yu | Mark-Christoph Müller
Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)

The ARRAU corpus is an anaphorically annotated corpus designed to cover a wide variety of aspects of anaphoric reference in a variety of genres, including both written text and spoken language. The objective of this annotation project is to push forward the state of the art in anaphoric annotation, by overcoming the limitations of current annotation practice and the scope of current models of anaphoric interpretation, which in turn may reveal other issues. The resulting corpus is still therefore very much a work in progress almost twenty years after the project started. In this paper, we discuss the issues identified with the coding scheme used for the previous release, ARRAU 2, and through the use of this corpus for three shared tasks; the proposed solutions to these issues; and the resulting corpus, ARRAU 3.


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Word-Level Alignment of Paper Documents with their Electronic Full-Text Counterparts
Mark-Christoph Müller | Sucheta Ghosh | Ulrike Wittig | Maja Rey
Proceedings of the 20th Workshop on Biomedical Language Processing

We describe a simple procedure for the automatic creation of word-level alignments between printed documents and their respective full-text versions. The procedure is unsupervised, uses standard, off-the-shelf components only, and reaches an F-score of 85.01 in the basic setup and up to 86.63 when using pre- and post-processing. Potential areas of application are manual database curation (incl. document triage) and biomedical expression OCR.


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Reconstructing Manual Information Extraction with DB-to-Document Backprojection: Experiments in the Life Science Domain
Mark-Christoph Müller | Sucheta Ghosh | Maja Rey | Ulrike Wittig | Wolfgang Müller | Michael Strube
Proceedings of the First Workshop on Scholarly Document Processing

We introduce a novel scientific document processing task for making previously inaccessible information in printed paper documents available to automatic processing. We describe our data set of scanned documents and data records from the biological database SABIO-RK, provide a definition of the task, and report findings from preliminary experiments. Rigorous evaluation proved challenging due to lack of gold-standard data and a difficult notion of correctness. Qualitative inspection of results, however, showed the feasibility and usefulness of the task

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pyMMAX2: Deep Access to MMAX2 Projects from Python
Mark-Christoph Müller
Proceedings of the 14th Linguistic Annotation Workshop

pyMMAX2 is an API for processing MMAX2 stand-off annotation data in Python. It provides a lightweight basis for the development of code which opens up the Java- and XML-based ecosystem of MMAX2 for more recent, Python-based NLP and data science methods. While pyMMAX2 is pure Python, and most functionality is implemented from scratch, the API re-uses the complex implementation of the essential business logic for MMAX2 annotation schemes by interfacing with the original MMAX2 Java libraries. pyMMAX2 is available for download at


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Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications
Mark-Christoph Mueller
RELATIONS - Workshop on meaning relations between phrases and sentences

We present a very simple, unsupervised method for the pairwise matching of documents from heterogeneous collections. We demonstrate our method with the Concept-Project matching task, which is a binary classification task involving pairs of documents from heterogeneous collections. Although our method only employs standard resources without any domain- or task-specific modifications, it clearly outperforms the more complex system of the original authors. In addition, our method is transparent, because it provides explicit information about how a similarity score was computed, and efficient, because it is based on the aggregation of (pre-computable) word-level similarities.

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HITS-SBD at the FinSBD Task: Machine Learning vs. Rule-based Sentence Boundary Detection
Mehwish Fatima | Mark-Christoph Mueller
Proceedings of the First Workshop on Financial Technology and Natural Language Processing


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Transparent, Efficient, and Robust Word Embedding Access with WOMBAT
Mark-Christoph Müller | Michael Strube
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

We present WOMBAT, a Python tool which supports NLP practitioners in accessing word embeddings from code. WOMBAT addresses common research problems, including unified access, scaling, and robust and reproducible preprocessing. Code that uses WOMBAT for accessing word embeddings is not only cleaner, more readable, and easier to reuse, but also much more efficient than code using standard in-memory methods: a Python script using WOMBAT for evaluating seven large word embedding collections (8.7M embedding vectors in total) on a simple SemEval sentence similarity task involving 250 raw sentence pairs completes in under ten seconds end-to-end on a standard notebook computer.


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Knowledge Sources for Bridging Resolution in Multi-Party Dialog
Mark-Christoph Mueller | Margot Mieskes | Michael Strube
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper we investigate the coverage of the two knowledge sources WordNet and Wikipedia for the task of bridging resolution. We report on an annotation experiment which yielded pairs of bridging anaphors and their antecedents in spoken multi-party dialog. Manual inspection of the two knowledge sources showed that, with some interesting exceptions, Wikipedia is superior to WordNet when it comes to the coverage of information necessary to resolve the bridging anaphors in our data set. We further describe a simple procedure for the automatic extraction of the required knowledge from Wikipedia by means of an API, and discuss some of the implications of the procedure’s performance.