Sarah Schulz


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Named Entities in Medical Case Reports: Corpus and Experiments
Sarah Schulz | Jurica Ševa | Samuel Rodriguez | Malte Ostendorff | Georg Rehm
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central’s open access library. In the case reports, we annotate cases, conditions, findings, factors and negation modifiers. Moreover, where applicable, we annotate relations between these entities. As such, this is the first corpus of this kind made available to the scientific community in English. It enables the initial investigation of automatic information extraction from case reports through tasks like Named Entity Recognition, Relation Extraction and (sentence/paragraph) relevance detection. Additionally, we present four strong baseline systems for the detection of medical entities made available through the annotated dataset.


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Towards Coreference for Literary Text: Analyzing Domain-Specific Phenomena
Ina Roesiger | Sarah Schulz | Nils Reiter
Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

Coreference resolution is the task of grouping together references to the same discourse entity. Resolving coreference in literary texts could benefit a number of Digital Humanities (DH) tasks, such as analyzing the depiction of characters and/or their relations. Domain-dependent training data has shown to improve coreference resolution for many domains, e.g. the biomedical domain, as its properties differ significantly from news text or dialogue, on which automatic systems are typically trained. Literary texts could also benefit from corpora annotated with coreference. We therefore analyze the specific properties of coreference-related phenomena on a number of texts and give directions for the adaptation of annotation guidelines. As some of the adaptations have profound impact, we also present a new annotation tool for coreference, with a focus on enabling annotation of long texts with many discourse entities.


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Multi-modular domain-tailored OCR post-correction
Sarah Schulz | Jonas Kuhn
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

One of the main obstacles for many Digital Humanities projects is the low data availability. Texts have to be digitized in an expensive and time consuming process whereas Optical Character Recognition (OCR) post-correction is one of the time-critical factors. At the example of OCR post-correction, we show the adaptation of a generic system to solve a specific problem with little data. The system accounts for a diversity of errors encountered in OCRed texts coming from different time periods in the domain of literature. We show that the combination of different approaches, such as e.g. Statistical Machine Translation and spell checking, with the help of a ranking mechanism tremendously improves over single-handed approaches. Since we consider the accessibility of the resulting tool as a crucial part of Digital Humanities collaborations, we describe the workflow we suggest for efficient text recognition and subsequent automatic and manual post-correction


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Learning from Within? Comparing PoS Tagging Approaches for Historical Text
Sarah Schulz | Jonas Kuhn
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we investigate unsupervised and semi-supervised methods for part-of-speech (PoS) tagging in the context of historical German text. We locate our research in the context of Digital Humanities where the non-canonical nature of text causes issues facing an Natural Language Processing world in which tools are mainly trained on standard data. Data deviating from the norm requires tools adjusted to this data. We explore to which extend the availability of such training material and resources related to it influences the accuracy of PoS tagging. We investigate a variety of algorithms including neural nets, conditional random fields and self-learning techniques in order to find the best-fitted approach to tackle data sparsity. Although methods using resources from related languages outperform weakly supervised methods using just a few training examples, we can still reach a promising accuracy with methods abstaining additional resources.

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Code-Switching Ubique Est - Language Identification and Part-of-Speech Tagging for Historical Mixed Text
Sarah Schulz | Mareike Keller
Proceedings of the 10th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

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Challenges of Computational Processing of Code-Switching
Özlem Çetinoğlu | Sarah Schulz | Ngoc Thang Vu
Proceedings of the Second Workshop on Computational Approaches to Code Switching


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Towards Shared Datasets for Normalization Research
Orphée De Clercq | Sarah Schulz | Bart Desmet | Véronique Hoste
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we present a Dutch and English dataset that can serve as a gold standard for evaluating text normalization approaches. With the combination of text messages, message board posts and tweets, these datasets represent a variety of user generated content. All data was manually normalized to their standard form using newly-developed guidelines. We perform automatic lexical normalization experiments on these datasets using statistical machine translation techniques. We focus on both the word and character level and find that we can improve the BLEU score with ca. 20% for both languages. In order for this user generated content data to be released publicly to the research community some issues first need to be resolved. These are discussed in closer detail by focussing on the current legislation and by investigating previous similar data collection projects. With this discussion we hope to shed some light on various difficulties researchers are facing when trying to share social media data.


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Normalization of Dutch User-Generated Content
Orphée De Clercq | Sarah Schulz | Bart Desmet | Els Lefever | Véronique Hoste
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013