Ina Roesiger

Also published as: Ina Rösiger


2018

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BASHI: A Corpus of Wall Street Journal Articles Annotated with Bridging Links
Ina Rösiger
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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German Radio Interviews: The GRAIN Release of the SFB732 Silver Standard Collection
Katrin Schweitzer | Kerstin Eckart | Markus Gärtner | Agnieszka Falenska | Arndt Riester | Ina Rösiger | Antje Schweitzer | Sabrina Stehwien | Jonas Kuhn
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Bridging resolution: Task definition, corpus resources and rule-based experiments
Ina Roesiger | Arndt Riester | Jonas Kuhn
Proceedings of the 27th International Conference on Computational Linguistics

Recent work on bridging resolution has so far been based on the corpus ISNotes (Markert et al. 2012), as this was the only corpus available with unrestricted bridging annotation. Hou et al. 2014’s rule-based system currently achieves state-of-the-art performance on this corpus, as learning-based approaches suffer from the lack of available training data. Recently, a number of new corpora with bridging annotations have become available. To test the generalisability of the approach by Hou et al. 2014, we apply a slightly extended rule-based system to these corpora. Besides the expected out-of-domain effects, we also observe low performance on some of the in-domain corpora. Our analysis shows that this is the result of two very different phenomena being defined as bridging, namely referential and lexical bridging. We also report that filtering out gold or predicted coreferent anaphors before applying the bridging resolution system helps improve bridging resolution.

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Anaphora Resolution with the ARRAU Corpus
Massimo Poesio | Yulia Grishina | Varada Kolhatkar | Nafise Moosavi | Ina Roesiger | Adam Roussel | Fabian Simonjetz | Alexandra Uma | Olga Uryupina | Juntao Yu | Heike Zinsmeister
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

The ARRAU corpus is an anaphorically annotated corpus of English providing rich linguistic information about anaphora resolution. The most distinctive feature of the corpus is the annotation of a wide range of anaphoric relations, including bridging references and discourse deixis in addition to identity (coreference). Other distinctive features include treating all NPs as markables, including non-referring NPs; and the annotation of a variety of morphosyntactic and semantic mention and entity attributes, including the genericity status of the entities referred to by markables. The corpus however has not been extensively used for anaphora resolution research so far. In this paper, we discuss three datasets extracted from the ARRAU corpus to support the three subtasks of the CRAC 2018 Shared Task–identity anaphora resolution over ARRAU-style markables, bridging references resolution, and discourse deixis; the evaluation scripts assessing system performance on those datasets; and preliminary results on these three tasks that may serve as baseline for subsequent research in these phenomena.

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Rule- and Learning-based Methods for Bridging Resolution in the ARRAU Corpus
Ina Roesiger
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

We present two systems for bridging resolution, which we submitted to the CRAC shared task on bridging anaphora resolution in the ARRAU corpus (track 2): a rule-based approach following Hou et al. 2014 and a learning-based approach. The re-implementation of Hou et al. 2014 achieves very poor performance when being applied to ARRAU. We found that the reasons for this lie in the different bridging annotations: whereas the rule-based system suggests many referential bridging pairs, ARRAU contains mostly lexical bridging. We describe the differences between these two types of bridging and adapt the rule-based approach to be able to handle lexical bridging. The modified rule-based approach achieves reasonable performance on all (sub)-tasks and outperforms a simple learning-based approach.

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Integrating Predictions from Neural-Network Relation Classifiers into Coreference and Bridging Resolution
Ina Roesiger | Maximilian Köper | Kim Anh Nguyen | Sabine Schulte im Walde
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

Cases of coreference and bridging resolution often require knowledge about semantic relations between anaphors and antecedents. We suggest state-of-the-art neural-network classifiers trained on relation benchmarks to predict and integrate likelihoods for relations. Two experiments with representations differing in noise and complexity improve our bridging but not our coreference resolver.

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Towards Bridging Resolution in German: Data Analysis and Rule-based Experiments
Janis Pagel | Ina Roesiger
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

Bridging resolution is the task of recognising bridging anaphors and linking them to their antecedents. While there is some work on bridging resolution for English, there is only little work for German. We present two datasets which contain bridging annotations, namely DIRNDL and GRAIN, and compare the performance of a rule-based system with a simple baseline approach on these two corpora. The performance for full bridging resolution ranges between an F1 score of 13.6% for DIRNDL and 11.8% for GRAIN. An analysis using oracle lists suggests that the system could, to a certain extent, benefit from ranking and re-ranking antecedent candidates. Furthermore, we investigate the importance of single features and show that the features used in our work seem promising for future bridging resolution approaches.

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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.

2017

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Improving coreference resolution with automatically predicted prosodic information
Ina Roesiger | Sabrina Stehwien | Arndt Riester | Ngoc Thang Vu
Proceedings of the Workshop on Speech-Centric Natural Language Processing

Adding manually annotated prosodic information, specifically pitch accents and phrasing, to the typical text-based feature set for coreference resolution has previously been shown to have a positive effect on German data. Practical applications on spoken language, however, would rely on automatically predicted prosodic information. In this paper we predict pitch accents (and phrase boundaries) using a convolutional neural network (CNN) model from acoustic features extracted from the speech signal. After an assessment of the quality of these automatic prosodic annotations, we show that they also significantly improve coreference resolution.

2016

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IMS HotCoref DE: A Data-driven Co-reference Resolver for German
Ina Roesiger | Jonas Kuhn
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents a data-driven co-reference resolution system for German that has been adapted from IMS HotCoref, a co-reference resolver for English. It describes the difficulties when resolving co-reference in German text, the adaptation process and the features designed to address linguistic challenges brought forth by German. We report performance on the reference dataset TüBa-D/Z and include a post-task SemEval 2010 evaluation, showing that the resolver achieves state-of-the-art performance. We also include ablation experiments that indicate that integrating linguistic features increases results. The paper also describes the steps and the format necessary to use the resolver on new texts. The tool is freely available for download.

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SciCorp: A Corpus of English Scientific Articles Annotated for Information Status Analysis
Ina Roesiger
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents SciCorp, a corpus of full-text English scientific papers of two disciplines, genetics and computational linguistics. The corpus comprises co-reference and bridging information as well as information status labels. Since SciCorp is annotated with both labels and the respective co-referent and bridging links, we believe it is a valuable resource for NLP researchers working on scientific articles or on applications such as co-reference resolution, bridging resolution or information status classification. The corpus has been reliably annotated by independent human coders with moderate inter-annotator agreement (average kappa = 0.71). In total, we have annotated 14 full papers containing 61,045 tokens and marked 8,708 definite noun phrases. The paper describes in detail the annotation scheme as well as the resulting corpus. The corpus is available for download in two different formats: in an offset-based format and for the co-reference annotations in the widely-used, tabular CoNLL-2012 format.

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Acquisition of semantic relations between terms: how far can we get with standard NLP tools?
Ina Roesiger | Julia Bettinger | Johannes Schäfer | Michael Dorna | Ulrich Heid
Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)

The extraction of data exemplifying relations between terms can make use, at least to a large extent, of techniques that are similar to those used in standard hybrid term candidate extraction, namely basic corpus analysis tools (e.g. tagging, lemmatization, parsing), as well as morphological analysis of complex words (compounds and derived items). In this article, we discuss the use of such techniques for the extraction of raw material for a description of relations between terms, and we provide internal evaluation data for the devices developed. We claim that user-generated content is a rich source of term variation through paraphrasing and reformulation, and that these provide relational data at the same time as term variants. Germanic languages with their rich word formation morphology may be particularly good candidates for the approach advocated here.

2015

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A Pilot Experiment on Exploiting Translations for Literary Studies on Kafka’s “Verwandlung”
Fabienne Cap | Ina Rösiger | Jonas Kuhn
Proceedings of the Fourth Workshop on Computational Linguistics for Literature

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Using prosodic annotations to improve coreference resolution of spoken text
Ina Roesiger | Arndt Riester
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Resolving Coreferent and Associative Noun Phrases in Scientific Text
Ina Roesiger | Simone Teufel
Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics