Michaela Regneri


2020

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Images and Imagination: Automated Analysis of Priming Effects Related to Autism Spectrum Disorder and Developmental Language Disorder
Michaela Regneri | Diane King | Fahreen Walji | Olympia Palikara
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Different aspects of language processing have been shown to be sensitive to priming but the findings of studies examining priming effects in adolescents with Autism Spectrum Disorder (ASD) and Developmental Language Disorder (DLD) have been inconclusive. We present a study analysing visual and implicit semantic priming in adolescents with ASD and DLD. Based on a dataset of fictional and script-like narratives, we evaluate how often and how extensively, content of two different priming sources is used by the participants. The first priming source was visual, consisting of images shown to the participants to assist them with their storytelling. The second priming source originated from commonsense knowledge, using crowdsourced data containing prototypical script elements. Our results show that individuals with ASD are less sensitive to both types of priming, but show typical usage of primed cues when they use them at all. In contrast, children with DLD show mostly average priming sensitivity, but exhibit an over-proportional use of the priming cues.

2016

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new/s/leak – Information Extraction and Visualization for Investigative Data Journalists
Seid Muhie Yimam | Heiner Ulrich | Tatiana von Landesberger | Marcel Rosenbach | Michaela Regneri | Alexander Panchenko | Franziska Lehmann | Uli Fahrer | Chris Biemann | Kathrin Ballweg
Proceedings of ACL-2016 System Demonstrations

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Automated Discourse Analysis of Narrations by Adolescents with Autistic Spectrum Disorder
Michaela Regneri | Diane King
Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning

2014

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lex4all: A language-independent tool for building and evaluating pronunciation lexicons for small-vocabulary speech recognition
Anjana Vakil | Max Paulus | Alexis Palmer | Michaela Regneri
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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SeedLing: Building and Using a Seed corpus for the Human Language Project
Guy Emerson | Liling Tan | Susanne Fertmann | Alexis Palmer | Michaela Regneri
Proceedings of the 2014 Workshop on the Use of Computational Methods in the Study of Endangered Languages

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Short-Term Projects, Long-Term Benefits: Four Student NLP Projects for Low-Resource Languages
Alexis Palmer | Michaela Regneri
Proceedings of the 2014 Workshop on the Use of Computational Methods in the Study of Endangered Languages

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Aligning Predicate-Argument Structures for Paraphrase Fragment Extraction
Michaela Regneri | Rui Wang | Manfred Pinkal
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Paraphrases and paraphrasing algorithms have been found of great importance in various natural language processing tasks. While most paraphrase extraction approaches extract equivalent sentences, sentences are an inconvenient unit for further processing, because they are too specific, and often not exact paraphrases. Paraphrase fragment extraction is a technique that post-processes sentential paraphrases and prunes them to more convenient phrase-level units. We present a new approach that uses semantic roles to extract paraphrase fragments from sentence pairs that share semantic content to varying degrees, including full paraphrases. In contrast to previous systems, the use of semantic parses allows for extracting paraphrases with high wording variance and different syntactic categories. The approach is tested on four different input corpora and compared to two previous systems for extracting paraphrase fragments. Our system finds three times as many good paraphrase fragments per sentence pair as the baselines, and at the same time outputs 30% fewer unrelated fragment pairs.

2013

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Grounding Action Descriptions in Videos
Michaela Regneri | Marcus Rohrbach | Dominikus Wetzel | Stefan Thater | Bernt Schiele | Manfred Pinkal
Transactions of the Association for Computational Linguistics, Volume 1

Recent work has shown that the integration of visual information into text-based models can substantially improve model predictions, but so far only visual information extracted from static images has been used. In this paper, we consider the problem of grounding sentences describing actions in visual information extracted from videos. We present a general purpose corpus that aligns high quality videos with multiple natural language descriptions of the actions portrayed in the videos, together with an annotation of how similar the action descriptions are to each other. Experimental results demonstrate that a text-based model of similarity between actions improves substantially when combined with visual information from videos depicting the described actions.

2012

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Using Discourse Information for Paraphrase Extraction
Michaela Regneri | Rui Wang
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Learning Script Participants from Unlabeled Data
Michaela Regneri | Alexander Koller | Josef Ruppenhofer | Manfred Pinkal
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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Learning Script Knowledge with Web Experiments
Michaela Regneri | Alexander Koller | Manfred Pinkal
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2008

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Regular Tree Grammars as a Formalism for Scope Underspecification
Alexander Koller | Michaela Regneri | Stefan Thater
Proceedings of ACL-08: HLT

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Efficient Processing of Underspecified Discourse Representations
Michaela Regneri | Markus Egg | Alexander Koller
Proceedings of ACL-08: HLT, Short Papers

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Underspecified Modelling of Complex Discourse Constraints
Markus Egg | Michaela Regneri
Coling 2008: Companion volume: Posters