Experiments on the Automatic Induction of German Semantic Verb Classes
Bibliographic Data
| ID | 12155652 |
|---|---|
| Authors | Sabine Schulte Im Walde (0000-0002-8975-6255, Saarland University, corresponding author) |
| Year | 2006 |
| Volume | 32 |
| Issue | 2 |
| Pages | 159-194 |
| Publication date | 2006-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Computational Linguistics (JOURNAL) |
| Journal identifiers | ISSN: 0891-2017 • E-ISSN: 1530-9312 |
| Publisher | Association for Computational Linguistics (PUBLISHER • US) |
| DOI | 10.1162/coli.2006.32.2.159 |
| OpenAlex | W2052474702 |
| Language | EN |
| Citations received | 15 |
| References cited | 20 |
This article presents clustering experiments on German verbs: A statistical grammar model for German serves as the source for a distributional verb description at the lexical syntax-semantics interface, and the unsupervised clustering algorithm k-means uses the empirical verb properties to perform an automatic induction of verb classes. Various evaluation measures are applied to compare the clustering results to gold standard German semantic verb classes under different criteria. The primary goals of the experiments are (1) to empirically utilize and investigate the well-established relationship between verb meaning and verb behavior within a cluster analysis and (2) to investigate the required technical parameters of a cluster analysis with respect to this specific linguistic task. The clustering methodology is developed on a small-scale verb set and then applied to a larger-scale verb set including 883 German verbs
Cluster analysis · German · Linguistics · Modal verb · Natural language processing · Programming language · Reflexive verb · Semantics (computer science · Syntax · Verb · Computer Science · Natural Language Processing Techniques · Speech and dialogue systems · Topic Modeling · Artificial Intelligence
Argument structure and text genre
Frequency, acceptability, and selection
Evaluation of the Competitiveness and Performance of Destinations Through Clustering Method Within the Scope of the Travel and Tourism Development Index
Polysemy—Evidence from Linguistics, Behavioral Science, and Contextualized Language Models
Activity knowledge discovery
Distributional Semantics and Linguistic Theory
Distributional Memory
The Proposition Bank
Statistical Metaphor Processing
Verb Classification Across Languages
Semantic Role Labeling
Verb Class Disambiguation Using Informative Priors
My Big, Fat 50-Year Journey
Word Sense Clustering and Clusterability
A Graph-Theoretic Framework for Semantic Distance
Semantics and cognition
Elements of Information Theory
Comparing partitions
Objective Criteria for the Evaluation of Clustering Methods
Hierarchical Grouping to Optimize an Objective Function
Wörterbuch zur Valenz und Distribution deutscher Verben
A Coefficient of Agreement for Nominal Scales
Detecting the Organization of Semantic Subclasses of Japanese Verbs
Eurowordnet
FrameNet and Frame Semantics
Class-Based Probability Estimation Using a Semantic Hierarchy
Automatic Verb Classification Based on Statistical Distributions of Argument Structure
Verb Class Disambiguation Using Informative Priors
Learning Methods to Combine Linguistic Indicators
The Proposition Bank
Introduction to WordNet
Distributional Structure
WordNet
| Unique citing works | 15 |
|---|---|
| Citations per year | 0,68 |
| Citation span | 2004 - 2025 (22) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 15 |