Papers by Siegfried Handschuh

12 papers
Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition (L18-1)

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Challenge: a conceptual model for dictionary definitions is used to construct a knowledge graph from natural language definitions.
Approach: They propose a method for automatically building a graph world knowledge base from natural language definitions.
Outcome: The proposed method was used in an interpretable text entailment recognition approach.
Graphene: a Context-Preserving Open Information Extraction System (C18-2)

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Challenge: Graphene is an open IE system that generates accurate, meaningful and complete propositions . current systems tend to extract propositions with long argument phrases that can be further decomposed into meaningful propositions, with each of them representing a separate fact.
Approach: They propose a lightweight Open IE system that generates accurate, meaningful propositions . they identify the rhetorical relations that hold between them to maintain their semantic relationship .
Outcome: The proposed system generates propositions that are accurate, meaningful and complete . it preserves the context of the relational tuples extracted from the source sentence .
Supporting Cognitive and Emotional Empathic Writing of Students (2021.acl-long)

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Challenge: Empathy skills are an elementary skill in society for daily interaction and professional communication and are therefore elementary for educational curricula.
Approach: They propose an annotation approach to capture emotional and cognitive empathy in student-written peer reviews on business models in germany.
Outcome: The proposed annotation scheme guides annotators to a substantial to moderate agreement with the model and shows that it is effective.
Transforming Complex Sentences into a Semantic Hierarchy (P19-1)

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Challenge: Existing approaches for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences are lacking.
Approach: They propose a method for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences.
Outcome: The proposed approach outperforms state-of-the-art approaches in MT and information extraction tasks.
A Multilingual Test Collection for the Semantic Search of Entity Categories (L18-1)

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Challenge: Despite the high popularity of entity search, entity categories have not received equal attention.
Approach: They propose to make public a multilingual test collection comprehending English, Portuguese and German to meet the demands of the entity search community.
Outcome: The proposed test collection comprehends English, Portuguese and German and provides comparative baselines and an analysis of the results.
When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs) (2023.starsem-1)

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Challenge: In this paper, we examine the ability of large language models (LLMs) to accommodate different pragmatic sentence types, such as questions, commands, and sentence fragments for natural language inference (NLI).
Approach: They propose to fine-tune large language models to accommodate different sentence types for natural language inference (NLI) they also explore ChatGPT's concept of entailment by using a symbolic semantic parser.
Outcome: The proposed models can accommodate different sentence types without losing too much accuracy on MNLI-matched models.
SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages (L18-1)

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Challenge: SemR-11 is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages.
Approach: This paper describes a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages.
Outcome: The dataset is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages.
A Corpus for Argumentative Writing Support in German (2020.coling-main)

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Challenge: In today's world most information is readily available. Consequently, the sole reproduction of information is losing attention.
Approach: They propose an annotation approach to capture claims and premises of arguments and their relations in student-written peer reviews on business models in german language.
Outcome: The proposed annotation scheme guides annotators to moderate agreement with the proposed scheme on 50 persuasive student-written peer reviews on business models.
Indra: A Word Embedding and Semantic Relatedness Server (L18-1)

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Challenge: Word embedding/distributional semantic models are a fundamental component in many natural language processing (NLP) architectures.
Approach: They propose a multi-lingual word embedding/distributional semantics framework which supports creation, use and evaluation of word embedded models.
Outcome: The proposed tool supports the creation, use and evaluation of word embedding models.
A Survey on Open Information Extraction (C18-1)

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Challenge: Existing approaches to open information extraction (Open IE) focus on narrow, well-defined requests over a predefined set of target relations on small, homogeneous corpora.
Approach: They propose to use unsupervised methods to extract all types of relations found in text . they propose to implement a system that can be automated to detect possible relations .
Outcome: The proposed approaches have been compared with existing methods and are based on the results of a literature review.
Context Matters: A Pragmatic Study of PLMs’ Negation Understanding (2022.acl-long)

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Challenge: In linguistics, there are two main perspectives on negation: a semantic and a pragmatic view.
Approach: They propose to use transformer-based pre-trained language models to study negation understanding using a pragmatic paradigm.
Outcome: The proposed transformer-based model outperforms the human benchmark at NLU and GLUE, and the results are much more optimistic than previous studies.
Graphene: Semantically-Linked Propositions in Open Information Extraction (C18-1)

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Challenge: Existing Open IE systems focus on identifying and extracting relations of interest, but this manual labor scales linearly with the number of target relations.
Approach: They propose an Open Information Extraction approach that uses a two-layered transformation stage and rhetorical relation identification to transform sentences into syntactically sound sentences.
Outcome: The proposed approach outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures.

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