Search Results for author: Siegfried Handschuh

Found 29 papers, 7 papers with code

Context Matters: A Pragmatic Study of PLMs’ Negation Understanding

no code implementations ACL 2022 Reto Gubelmann, Siegfried Handschuh

In linguistics, there are two main perspectives on negation: a semantic and a pragmatic view.

Number of Attention Heads vs Number of Transformer-Encoders in Computer Vision

no code implementations15 Sep 2022 Tomas Hrycej, Bernhard Bermeitinger, Siegfried Handschuh

Determining an appropriate number of attention heads on one hand and the number of transformer-encoders, on the other hand, is an important choice for Computer Vision (CV) tasks using the Transformer architecture.

Training Neural Networks in Single vs Double Precision

no code implementations15 Sep 2022 Tomas Hrycej, Bernhard Bermeitinger, Siegfried Handschuh

For strongly nonlinear tasks, both algorithm classes find only solutions fairly poor in terms of mean square error as related to the output variance.

Uncovering More Shallow Heuristics: Probing the Natural Language Inference Capacities of Transformer-Based Pre-Trained Language Models Using Syllogistic Patterns

no code implementations19 Jan 2022 Reto Gubelmann, Siegfried Handschuh

In this article, we explore the shallow heuristics used by transformer-based pre-trained language models (PLMs) that are fine-tuned for natural language inference (NLI).

Natural Language Inference

Exploring the Promises of Transformer-Based LMs for the Representation of Normative Claims in the Legal Domain

no code implementations25 Aug 2021 Reto Gubelmann, Peter Hongler, Siegfried Handschuh

In this article, we explore the potential of transformer-based language models (LMs) to correctly represent normative statements in the legal domain, taking tax law as our use case.

Supporting Cognitive and Emotional Empathic Writing of Students

1 code implementation ACL 2021 Thiemo Wambsganss, Christina Niklaus, Matthias Söllner, Siegfried Handschuh, Jan Marco Leimeister

We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components.

Context-Preserving Text Simplification

1 code implementation24 May 2021 Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh

We present a context-preserving text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences.

Text Simplification

A Corpus for Argumentative Writing Support in German

1 code implementation COLING 2020 Thiemo Wambsganss, Christina Niklaus, Matthias Söllner, Siegfried Handschuh, Jan Marco Leimeister

In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language.

XTE: Explainable Text Entailment

no code implementations25 Sep 2020 Vivian S. Silva, André Freitas, Siegfried Handschuh

Text entailment, the task of determining whether a piece of text logically follows from another piece of text, is a key component in NLP, providing input for many semantic applications such as question answering, text summarization, information extraction, and machine translation, among others.

Machine Translation Question Answering +1

Multimodal Semantic Transfer from Text to Image. Fine-Grained Image Classification by Distributional Semantics

no code implementations7 Jan 2020 Simon Donig, Maria Christoforaki, Bernhard Bermeitinger, Siegfried Handschuh

In the last years, image classification processes like neural networks in the area of art-history and Heritage Informatics have experienced a broad distribution (Lang and Ommer 2018).

Fine-Grained Image Classification General Classification

DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor English and German

1 code implementation26 Sep 2019 Christina Niklaus, Matthias Cetto, Andre Freitas, Siegfried Handschuh

We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications.

Text Simplification

MinWikiSplit: A Sentence Splitting Corpus with Minimal Propositions

no code implementations WS 2019 Christina Niklaus, Andre Freitas, Siegfried Handschuh

We compiled a new sentence splitting corpus that is composed of 203K pairs of aligned complex source and simplified target sentences.

Text Simplification

Representational Capacity of Deep Neural Networks -- A Computing Study

no code implementations19 Jul 2019 Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh

This does not directly contradict the theoretical findings---it is possible that the superior representational capacity of deep networks is genuine while finding the mean square minimum of such deep networks is a substantially harder problem than with shallow ones.

On the Semantic Interpretability of Artificial Intelligence Models

no code implementations9 Jul 2019 Vivian S. Silva, André Freitas, Siegfried Handschuh

Artificial Intelligence models are becoming increasingly more powerful and accurate, supporting or even replacing humans' decision making.

Decision Making

Singular Value Decomposition and Neural Networks

no code implementations27 Jun 2019 Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh

Singular Value Decomposition (SVD) constitutes a bridge between the linear algebra concepts and multi-layer neural networks---it is their linear analogy.

Transforming Complex Sentences into a Semantic Hierarchy

1 code implementation ACL 2019 Christina Niklaus, Matthias Cetto, Andre Freitas, Siegfried Handschuh

We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE).

Machine Translation Text Simplification +1

Graphene: A Context-Preserving Open Information Extraction System

1 code implementation COLING 2018 Matthias Cetto, Christina Niklaus, André Freitas, Siegfried Handschuh

In that way, we preserve the context of the relational tuples extracted from a source sentence, generating a novel lightweight semantic representation for Open IE that enhances the expressiveness of the extracted propositions.

Open Information Extraction

Graphene: Semantically-Linked Propositions in Open Information Extraction

1 code implementation COLING 2018 Matthias Cetto, Christina Niklaus, André Freitas, Siegfried Handschuh

We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification.

Open Information Extraction

Categorization of Semantic Roles for Dictionary Definitions

no code implementations WS 2016 Vivian S. Silva, Siegfried Handschuh, André Freitas

Understanding the semantic relationships between terms is a fundamental task in natural language processing applications.

Word Tagging with Foundational Ontology Classes: Extending the WordNet-DOLCE Mapping to Verbs

no code implementations20 Jun 2018 Vivian S. Silva, André Freitas, Siegfried Handschuh

Semantic annotation is fundamental to deal with large-scale lexical information, mapping the information to an enumerable set of categories over which rules and algorithms can be applied, and foundational ontology classes can be used as a formal set of categories for such tasks.

Semantic Relation Classification: Task Formalisation and Refinement

no code implementations WS 2016 Vivian S. Silva, Manuela Hürliman, Brian Davis, Siegfried Handschuh, André Freitas

This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora.

Classification General Classification +1

Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition

no code implementations LREC 2018 Vivian S. Silva, André Freitas, Siegfried Handschuh

Adopting a conceptual model composed of a set of semantic roles for dictionary definitions, we trained a classifier for automatically labeling definitions, preparing the data to be later converted to a graph representation.

A Survey on Open Information Extraction

no code implementations COLING 2018 Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh

We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction.

Open Information Extraction

Composite Semantic Relation Classification

no code implementations16 May 2018 Siamak Barzegar, Andre Freitas, Siegfried Handschuh, Brian Davis

Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text.

BIG-bench Machine Learning Classification +3

DINFRA: A One Stop Shop for Computing Multilingual Semantic Relatedness

no code implementations16 May 2018 Siamak Barzegar, Juliano Efson Sales, Andre Freitas, Siegfried Handschuh, Brian Davis

This demonstration presents an infrastructure for computing multilingual semantic relatedness and correlation for twelve natural languages by using three distributional semantic models (DSMs).

Object Classification in Images of Neoclassical Artifacts Using Deep Learning

no code implementations13 Oct 2017 Bernhard Bermeitinger, Maria Christoforaki, Simon Donig, Siegfried Handschuh

In this paper, we report on our efforts for using Deep Learning for classifying artifacts and their features in digital visuals as a part of the Neoclassica framework.

Classification General Classification

A Sentence Simplification System for Improving Relation Extraction

no code implementations COLING 2016 Christina Niklaus, Bernhard Bermeitinger, Siegfried Handschuh, André Freitas

In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems.

Relation Extraction Text Simplification

Cannot find the paper you are looking for? You can Submit a new open access paper.