Gerhard Heyer


2021

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Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning
Christopher Schröder | Kim Bürgl | Yves Annanias | Andreas Niekler | Lydia Müller | Daniel Wiegreffe | Christian Bender | Christoph Mengs | Gerik Scheuermann | Gerhard Heyer
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Open pit mines left many regions worldwide inhospitable or uninhabitable. Many sites are left behind in a hazardous or contaminated state, show remnants of waste, or have other restrictions imposed upon them, e.g., for the protection of human or nature. Such information has to be permanently managed in order to reuse those areas in the future. In this work we present and evaluate an automated workflow for supporting the post-mining management of former lignite open pit mines in the eastern part of Germany, where prior to any planned land reuse, aforementioned information has to be acquired to ensure the safety and validity of such an endeavor. Usually, this information is found in expert reports, either in the form of paper documents, or in the best case as digitized unstructured text—all of them in German language. However, due to the size and complexity of these documents, any inquiry is tedious and time-consuming, thereby slowing down or even obstructing the reuse of related areas. Since no training data is available, we employ active learning in order to perform multi-label sentence classification for two categories of restrictions and seven categories of topics. The final system integrates optical character recognition (OCR), active-learning-based text classification, and geographic information system visualization in order to effectively extract, query, and visualize this information for any area of interest. Active learning and text classification results are twofold: Whereas the restriction categories were reasonably accurate (>0.85 F1), the seven topic-oriented categories seemed to be complex even for human annotators and achieved mediocre evaluation scores (<0.70 F1).

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Casting the Same Sentiment Classification Problem
Erik Körner | Ahmad Dawar Hakimi | Gerhard Heyer | Martin Potthast
Findings of the Association for Computational Linguistics: EMNLP 2021

We introduce and study a problem variant of sentiment analysis, namely the “same sentiment classification problem”, where, given a pair of texts, the task is to determine if they have the same sentiment, disregarding the actual sentiment polarity. Among other things, our goal is to enable a more topic-agnostic sentiment classification. We study the problem using the Yelp business review dataset, demonstrating how sentiment data needs to be prepared for this task, and then carry out sequence pair classification using the BERT language model. In a series of experiments, we achieve an accuracy above 83% for category subsets across topics, and 89% on average.

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On Classifying whether Two Texts are on the Same Side of an Argument
Erik Körner | Gregor Wiedemann | Ahmad Dawar Hakimi | Gerhard Heyer | Martin Potthast
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed. In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task. We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task’s hypothesis. We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario. Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.

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Press Freedom Monitor: Detection of Reported Press and Media Freedom Violations in Twitter and News Articles
Tariq Yousef | Antje Schlaf | Janos Borst | Andreas Niekler | Gerhard Heyer
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Freedom of the press and media is of vital importance for democratically organised states and open societies. We introduce the Press Freedom Monitor, a tool that aims to detect reported press and media freedom violations in news articles and tweets. It is used by press and media freedom organisations to support their daily monitoring and to trigger rapid response actions. The Press Freedom Monitor enables the monitoring experts to get a fast overview over recently reported incidents and it has shown an impressive performance in this regard. This paper presents our work on the tool, starting with the training phase, which comprises defining the topic-related keywords to be used for querying APIs for news and Twitter content and evaluating different machine learning models based on a training dataset specifically created for our use case. Then, we describe the components of the production pipeline, including data gathering, duplicates removal, country mapping, case mapping and the user interface. We also conducted a usability study to evaluate the effectiveness of the user interface, and describe improvement plans for future work.

2018

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ILCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data
Andreas Niekler | Arnim Bleier | Christian Kahmann | Lisa Posch | Gregor Wiedemann | Kenan Erdogan | Gerhard Heyer | Markus Strohmaier
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features
Gregor Wiedemann | Gerhard Heyer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2014

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A New Implementation for Canonical Text Services
Jochen Tiepmar | Christoph Teichmann | Gerhard Heyer | Monica Berti | Gregory Crane
Proceedings of the 8th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH)

2010

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SentiWS - A Publicly Available German-language Resource for Sentiment Analysis
Robert Remus | Uwe Quasthoff | Gerhard Heyer
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, opinion mining etc. It lists positive and negative sentiment bearing words weighted within the interval of [-1; 1] plus their part of speech tag, and if applicable, their inflections. The current version of SentiWS (v1.8b) contains 1,650 negative and 1,818 positive words, which sum up to 16,406 positive and 16,328 negative word forms, respectively. It not only contains adjectives and adverbs explicitly expressing a sentiment, but also nouns and verbs implicitly containing one. The present work describes the resource’s structure, the three sources utilised to assemble it and the semi-supervised method incorporated to weight the strength of its entries. Furthermore the resource’s contents are extensively evaluated using a German-language evaluation set we constructed. The evaluation set is verified being reliable and its shown that SentiWS provides a beneficial lexical resource for German-language sentiment analysis related tasks to build on.

2008

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ASV Toolbox: a Modular Collection of Language Exploration Tools
Chris Biemann | Uwe Quasthoff | Gerhard Heyer | Florian Holz
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

ASV Toolbox is a modular collection of tools for the exploration of written language data both for scientific and educational purposes. It includes modules that operate on word lists or texts and allow to perform various linguistic annotation, classification and clustering tasks, including language detection, POS-tagging, base form reduction, named entity recognition, and terminology extraction. On a more abstract level, the algorithms deal with various kinds of word similarity, using pattern-based and statistical approaches. The collection can be used to work on large real-world data sets as well as for studying the underlying algorithms. Each module of the ASV Toolbox is designed to work either on a plain text files or with a connection to a MySQL database. While it is especially designed to work with corpora of the Leipzig Corpora Collection, it can easily be adapted to other sources.

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Tapping Huge Temporally Indexed Textual Resources with WCTAnalyze
Sebastian Gottwald | Matthias Richter | Gerhard Heyer | Gerik Scheuermann
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

WCTAnalyze is a tool for storing, accessing and visually analyzing huge collections of temporally indexed data. It is motivated by applications in media analysis, business intelligence etc. where higher level analysis is performed on top of linguistically and statistically processed unstructured textual data. WCTAnalyze combines fast access with economically storage behaviour and appropriates a lot of built in visualization options for result presentation in detail as well as in contrast. So it enables an efficient and effective way to explore chronological text patterns of word forms, their co-occurrence sets and co-occurrence set intersections. Digging deep into co-occurrences of the same semantic or syntactic describing wordforms, some entities can be recognized as to be temporal related, whereas other differ significantly. This behaviour motivates approaches in interactive discovering events based on co-occurrence subsets.

2002

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Information Extraction from Text Corpora: Using Filters on Collocation Sets
Gerhard Heyer | Uwe Quasthoff | Christian Wolff
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

1992

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Extended Spelling Correction for German
Ralf Kese | Friedrich Dudda | Gerhard Heyer | Marianne Kugler
Third Conference on Applied Natural Language Processing

1990

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Knowledge Representation and Semantics in a Complex Domain: The UNIX Natural Language Help System GOETHE
Gerhard Heyer | Ralf Kese | Frank Oemig | Friedrich Dudda
COLING 1990 Volume 3: Papers presented to the 13th International Conference on Computational Linguistics