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Found 33 papers, 7 papers with code
Graphene: Semantically-Linked Propositions in Open Information Extraction
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.
Graphene: A Context-Preserving Open Information Extraction System
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.
Transforming Complex Sentences into a Semantic Hierarchy
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).
DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor English and German
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.
Context-Preserving Text Simplification
We present a context-preserving text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences.
A Corpus for Argumentative Writing Support in German
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.
Supporting Cognitive and Emotional Empathic Writing of Students
We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components.
A Survey on Open Information Extraction
We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction.
DINFRA: A One Stop Shop for Computing Multilingual Semantic Relatedness
This demonstration presents an infrastructure for computing multilingual semantic relatedness and correlation for twelve natural languages by using three distributional semantic models (DSMs).
Semantic Relatedness for All (Languages): A Comparative Analysis of Multilingual Semantic Relatedness Using Machine Translation
The results also show that the benefit of using the most informative corpus outweighs the possible errors introduced by the machine translation.
Composite Semantic Relation Classification
Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text.
Object Classification in Images of Neoclassical Artifacts Using Deep Learning
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.
A Sentence Simplification System for Improving Relation Extraction
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.
Object classification in images of Neoclassical furniture using Deep Learning
This short paper outlines research results on object classification in images of Neoclassical furniture.
Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition
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.
Semantic Relation Classification: Task Formalisation and Refinement
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.
Categorization of Semantic Roles for Dictionary Definitions
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
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.
Singular Value Decomposition and Neural Networks
Singular Value Decomposition (SVD) constitutes a bridge between the linear algebra concepts and multi-layer neural networks---it is their linear analogy.
On the Semantic Interpretability of Artificial Intelligence Models
Artificial Intelligence models are becoming increasingly more powerful and accurate, supporting or even replacing humans' decision making.
Representational Capacity of Deep Neural Networks -- A Computing Study
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.
MinWikiSplit: A Sentence Splitting Corpus with Minimal Propositions
We compiled a new sentence splitting corpus that is composed of 203K pairs of aligned complex source and simplified target sentences.
Multimodal Semantic Transfer from Text to Image. Fine-Grained Image Classification by Distributional Semantics
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).
XTE: Explainable Text Entailment
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.
Exploring the Promises of Transformer-Based LMs for the Representation of Normative Claims in the Legal Domain
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.
Uncovering More Shallow Heuristics: Probing the Natural Language Inference Capacities of Transformer-Based Pre-Trained Language Models Using Syllogistic Patterns
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).
Context Matters: A Pragmatic Study of PLMs’ Negation Understanding
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
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
For strongly nonlinear tasks, both algorithm classes find only solutions fairly poor in terms of mean square error as related to the output variance.
Shallow Discourse Parsing for Open Information Extraction and Text Simplification
We present a discourse-aware text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences.
Analyzing FOMC Minutes: Accuracy and Constraints of Language Models
This research article analyzes the language used in the official statements released by the Federal Open Market Committee (FOMC) after its scheduled meetings to gain insights into the impact of FOMC official statements on financial markets and economic forecasting.
Discourse-Aware Text Simplification: From Complex Sentences to Linked Propositions
In that way, we generate a semantic hierarchy of minimal propositions that leads to a novel representation of complex assertions that puts a semantic layer on top of the simplified sentences.
Make Deep Networks Shallow Again
A stack of residual connection layers can be expressed as an expansion of terms similar to the Taylor expansion.
