Sebastian Pado
Biographic Data
| ID | 1629977 |
|---|---|
| NAME | Sebastian Pado |
| GIVEN NAMES | Sebastian |
| FAMILY NAME | Pado |
| SIGNATURE | PADO S |
| AFFILIATIONS | University of Stuttgart |
| ORCID | 0000-0002-7529-6825 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 36 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2007 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 3 |
Language Learning, Representation, and Processing in Humans and Machines: Introduction to the Special Issue
Large Language Models (LLMs) and humans acquire knowledge about language without direct supervision. LLMs do so by means of specific training objectives, while humans rely on sensory experience and social interaction. This parallelism has created a feeling in NLP and cognitive science that a systematic understanding of how LLMs acquire and use the encoded knowledge could provide useful insights for studying human cognition. Conversely, methods an…
Grounding semantic transparency in context: A distributional semantic study on German event nominalizations
We present the results of a large-scale corpus-based comparison of two German event nominalization patterns: deverbal nouns in -ung (e.g., die Evaluierung, 'the evaluation') and nominal infinitives (e.g., das Evaluieren, 'the evaluating'). Among the many available event nominalization patterns for German, we selected these two because they are both highly productive and challenging from the semantic point of view. Both patterns are known to keep …
Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets
This article investigates the integration of machine learning in the political claim annotation workflow with the goal to partially automate the annotation and analysis of large text corpora. It introduces the MARDY annotation environment and presents results from an experiment in which the annotation quality of annotators with and without machine learning based annotation support is compared. The design and setting aim to measure and evaluate: a…
Measuring Historical Emotions and Their Evolution: "An Interdisciplinary Endeavour to Investigate The ‘Emotions of Encounter’ | Medindo emoções históricas e sua evolução: um esforço interdisciplinar p…
The empirical study of emotions in Spanish travelogues and reports requires cultural knowledge as well as the use of linguistic annotation and quantitative methods. We report on an interdisciplinary project in which we perform emotion annotation on a selection of texts spanning several centuries to analyze the differences across different time slices. We show that indeed the emotional connotation changes qualitatively and quantitatively. Next to …
FrameNet’s Using relation as a source of concept-based paraphrases
Characterizing paraphrases formally has proven to be a challenging task. Hasegawa et al. (2011) pointed out the usefulness of FrameNet for paraphrase research, focusing on paraphrases which are backed by underlying classical linguistic relationships such as synonymy or voice alternations. This article proposes that other frame-to-frame-relations, notably Using , can serve as a source for concept-based paraphrases – that is, paraphrases that are b…
A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences
We present a vector space–based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, …
Dependency-Based Construction of Semantic Space Models
Traditionally, vector-based semantic space models use word co-occurrence counts from large corpora to represent lexical meaning. In this article we present a novel framework for constructing semantic spaces that takes syntactic relations into account. We introduce a formalization for this class of models, which allows linguistic knowledge to guide the construction process. We evaluate our framework on a range of tasks relevant for cognitive scien…
Dependency-Based Construction of Semantic Space Models
Traditionally, vector-based semantic space models use word co-occurrence counts from large corpora to represent lexical meaning. In this article we present a novel framework for constructing semantic spaces that takes syntactic relations into account. We introduce a formalization for this class of models, which allows linguistic knowledge to guide the construction process. We evaluate our framework on a range of tasks relevant for cognitive scien…
A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences
We present a vector space–based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, …
Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets
This article investigates the integration of machine learning in the political claim annotation workflow with the goal to partially automate the annotation and analysis of large text corpora. It introduces the MARDY annotation environment and presents results from an experiment in which the annotation quality of annotators with and without machine learning based annotation support is compared. The design and setting aim to measure and evaluate: a…
Language Learning, Representation, and Processing in Humans and Machines: Introduction to the Special Issue
Large Language Models (LLMs) and humans acquire knowledge about language without direct supervision. LLMs do so by means of specific training objectives, while humans rely on sensory experience and social interaction. This parallelism has created a feeling in NLP and cognitive science that a systematic understanding of how LLMs acquire and use the encoded knowledge could provide useful insights for studying human cognition. Conversely, methods an…
Grounding semantic transparency in context: A distributional semantic study on German event nominalizations
We present the results of a large-scale corpus-based comparison of two German event nominalization patterns: deverbal nouns in -ung (e.g., die Evaluierung, 'the evaluation') and nominal infinitives (e.g., das Evaluieren, 'the evaluating'). Among the many available event nominalization patterns for German, we selected these two because they are both highly productive and challenging from the semantic point of view. Both patterns are known to keep …
Dependency-Based Construction of Semantic Space Models
Traditionally, vector-based semantic space models use word co-occurrence counts from large corpora to represent lexical meaning. In this article we present a novel framework for constructing semantic spaces that takes syntactic relations into account. We introduce a formalization for this class of models, which allows linguistic knowledge to guide the construction process. We evaluate our framework on a range of tasks relevant for cognitive scien…
A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences
We present a vector space–based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, …
FrameNet’s Using relation as a source of concept-based paraphrases
Characterizing paraphrases formally has proven to be a challenging task. Hasegawa et al. (2011) pointed out the usefulness of FrameNet for paraphrase research, focusing on paraphrases which are backed by underlying classical linguistic relationships such as synonymy or voice alternations. This article proposes that other frame-to-frame-relations, notably Using , can serve as a source for concept-based paraphrases – that is, paraphrases that are b…
Measuring Historical Emotions and Their Evolution: "An Interdisciplinary Endeavour to Investigate The ‘Emotions of Encounter’ | Medindo emoções históricas e sua evolução: um esforço interdisciplinar p…
The empirical study of emotions in Spanish travelogues and reports requires cultural knowledge as well as the use of linguistic annotation and quantitative methods. We report on an interdisciplinary project in which we perform emotion annotation on a selection of texts spanning several centuries to analyze the differences across different time slices. We show that indeed the emotional connotation changes qualitatively and quantitatively. Next to …
Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets
This article investigates the integration of machine learning in the political claim annotation workflow with the goal to partially automate the annotation and analysis of large text corpora. It introduces the MARDY annotation environment and presents results from an experiment in which the annotation quality of annotators with and without machine learning based annotation support is compared. The design and setting aim to measure and evaluate: a…
Grounding semantic transparency in context: A distributional semantic study on German event nominalizations
We present the results of a large-scale corpus-based comparison of two German event nominalization patterns: deverbal nouns in -ung (e.g., die Evaluierung, 'the evaluation') and nominal infinitives (e.g., das Evaluieren, 'the evaluating'). Among the many available event nominalization patterns for German, we selected these two because they are both highly productive and challenging from the semantic point of view. Both patterns are known to keep …
Language Learning, Representation, and Processing in Humans and Machines: Introduction to the Special Issue
Large Language Models (LLMs) and humans acquire knowledge about language without direct supervision. LLMs do so by means of specific training objectives, while humans rely on sensory experience and social interaction. This parallelism has created a feeling in NLP and cognitive science that a systematic understanding of how LLMs acquire and use the encoded knowledge could provide useful insights for studying human cognition. Conversely, methods an…
Artificial Intelligence (7 works) · Computer Science (7 works) · Natural language processing (6 works) · Topic Modeling (6 works) · Natural Language Processing Techniques (5 works) · Linguistics (4 works) · Annotation (3 works) · Distributional semantics (2 works) · Mathematics (2 works) · Philosophy (2 works)