Manfred Klenner


2021

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Getting Hold of Villains and other Rogues
Manfred Klenner | Anne Göhring | Sophia Conrad
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

In this paper, we introduce the first corpus specifying negative entities within sentences. We discuss indicators for their presence, namely particular verbs, but also the linguistic conditions when their prediction should be suppressed. We further show that a fine-tuned Bert-based baseline model outperforms an over-generating rule-based approach which is not aware of these further restrictions. If a perfect filter were applied, both would be on par.

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DeInStance: Creating and Evaluating a German Corpus for Fine-Grained Inferred Stance Detection
Anne Göhring | Manfred Klenner | Sophia Conrad
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

2017

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Stance Detection in Facebook Posts of a German Right-wing Party
Manfred Klenner | Don Tuggener | Simon Clematide
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

We argue that in order to detect stance, not only the explicit attitudes of the stance holder towards the targets are crucial. It is the whole narrative the writer drafts that counts, including the way he hypostasizes the discourse referents: as benefactors or villains, as victims or beneficiaries. We exemplify the ability of our system to identify targets and detect the writer’s stance towards them on the basis of about 100 000 Facebook posts of a German right-wing party. A reader and writer model on top of our verb-based attitude extraction directly reveal stance conflicts.

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An Object-oriented Model of Role Framing and Attitude Prediction
Manfred Klenner
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

2016

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Sentiframes: A Resource for Verb-centered German Sentiment Inference
Manfred Klenner | Michael Amsler
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, a German verb resource for verb-centered sentiment inference is introduced and evaluated. Our model specifies verb polarity frames that capture the polarity effects on the fillers of the verb’s arguments given a sentence with that verb frame. Verb signatures and selectional restrictions are also part of the model. An algorithm to apply the verb resource to treebank sentences and the results of our first evaluation are discussed.

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An Hymn of an even Deeper Sentiment Analysis
Manfred Klenner
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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How Factuality Determines Sentiment Inferences
Manfred Klenner | Simon Clematide
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

2015

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Verb-centered Sentiment Inference with Description Logics
Manfred Klenner
Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2014

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Inducing Domain-specific Noun Polarity Guided by Domain-independent Polarity Preferences of Adjectives
Manfred Klenner | Michael Amsler | Nora Hollenstein
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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SA-UZH: Verb-based Sentiment Analysis
Nora Hollenstein | Michael Amsler | Martina Bachmann | Manfred Klenner
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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A Pilot Study on the Semantic Classification of Two German Prepositions: Combining Monolingual and Multilingual Evidence
Simon Clematide | Manfred Klenner
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

2012

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MLSA — A Multi-layered Reference Corpus for German Sentiment Analysis
Simon Clematide | Stefan Gindl | Manfred Klenner | Stefanos Petrakis | Robert Remus | Josef Ruppenhofer | Ulli Waltinger | Michael Wiegand
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In this paper, we describe MLSA, a publicly available multi-layered reference corpus for German-language sentiment analysis. The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity. The sentence-layer annotation, as the most coarse-grained annotation, focuses on aspects of objectivity, subjectivity and the overall polarity of the respective sentences. Layer 2 is concerned with polarity on the word- and phrase-level, annotating both subjective and factual language. The annotations on Layer 3 focus on the expression-level, denoting frames of private states such as objective and direct speech events. These three layers and their respective annotations are intended to be fully independent of each other. At the same time, exploring for and discovering interactions that may exist between different layers should also be possible. The reliability of the respective annotations was assessed using the average pairwise agreement and Fleiss' multi-rater measures. We believe that MLSA is a beneficial resource for sentiment analysis research, algorithms and applications that focus on the German language.

2011

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An Incremental Model for the Coreference Resolution Task of BioNLP 2011
Don Tuggener | Manfred Klenner | Gerold Schneider | Simon Clematide | Fabio Rinaldi
Proceedings of BioNLP Shared Task 2011 Workshop

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An Incremental Model for Coreference Resolution with Restrictive Antecedent Accessibility
Manfred Klenner | Don Tuggener
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

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An Incremental Entity-Mention Model for Coreference Resolution with Restrictive Antecedent Accessibility
Manfred Klenner | Don Tuggener
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods
Vassiliki Rentoumi | Stefanos Petrakis | Manfred Klenner | George A. Vouros | Vangelis Karkaletsis
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope well with figurative language could not always reach a certain decision about the polarity orientation of sentences, yielding erroneous evaluations. We support the conjecture that these cases bearing mild figurativeness could be better handled by a rule-based system. These two systems, acting complementarily, could bridge the gap between machine learning and rule-based approaches. Experimental results using the corpus of the Affective Text Task of SemEval ’07, provide evidence in favor of this direction.

2009

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Optimization in Coreference Resolution is not Needed: A Nearly-Optimal Algorithm with Intensional Constraints
Manfred Klenner | Étienne Ailloud
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Vers des contraintes plus linguistiques en résolution de coréférences
Étienne Ailloud | Manfred Klenner
Actes de la 16ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Nous proposons un modèle filtrant de résolution de coréférences basé sur les notions de transitivité et d’exclusivité linguistique. À partir de l’hypothèse générale que les chaînes de coréférence demeurent cohérentes tout au long d’un texte, notre modèle assure le respect de certaines contraintes linguistiques (via des filtres) quant à la coréférence, ce qui améliore la résolution globale. Le filtrage a lieu à différentes étapes de l’approche standard (c-à-d. par apprentissage automatique), y compris avant l’apprentissage et avant la classification, accélérant et améliorant ce processus.

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Composition multilingue de sentiments
Stefanos Petrakis | Manfred Klenner | Étienne Ailloud | Angela Fahrni
Actes de la 16ème conférence sur le Traitement Automatique des Langues Naturelles. Démonstrations

Nous présentons ici PolArt, un outil multilingue pour l’analyse de sentiments qui aborde la composition des sentiments en appliquant des transducteurs en cascade. La compositionnalité est assurée au moyen de polarités préalables extraites d’un lexique et des règles de composition appliquées de manière incrémentielle.

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PolArt: A Robust Tool for Sentiment Analysis
Manfred Klenner | Angela Fahrni | Stefanos Petrakis
Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009)

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Robust Compositional Polarity Classification
Manfred Klenner | Stefanos Petrakis | Angela Fahrni
Proceedings of the International Conference RANLP-2009

2007

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Shallow Dependency Labeling
Manfred Klenner
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

2006

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Grammatical Role Labeling with Integer Linear Programming
Manfred Klenner
Demonstrations

2004

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Steps Towards Semantically Annotated Language Resources
Manfred Klenner | Fabio Rinaldi | Michael Hess
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)