GermaNet is a wordnet for German

A wordnet groups synonyms into synsets and represents relations between synsets. The hypernym relation forms a hierarchical graph structure.

Rover is a web application for GermaNet

Rover displays the data in GermaNet in an interactive interface designed for researchers. It offers:

• advanced searching for synsets
• visualizing the hypernym graph
• calculating synsets’ semantic relatedness via graph-based measures

Synset Search Overview

Try it: https://weblicht.sfs.uni-tuebingen.de/rover/search

Search options

Rover supports advanced searches for synsets:

• search by regular expression and edit distance
• restrict results by grammatical category, semantic class, and orthographic variant

Results list

A summary of each synset in the search results includes:

• words in the synset
• its semantic class
• associated Wiktionary definitions
• summary of conceptual relations to other synsets

Conceptual Relations

Selecting a synset displays details about its conceptual relations:

• a network diagram of the synset’s hypernyms
• related synsets, displayed as navigation buttons

Lexical Units and Relations

More details about the words in the selected synset:

• lexically-related words, displayed as navigation buttons
• Interlingual index records (pointers to Princeton WordNet)
• examples and associated frame types
• decomposition of compound nouns

Semantic Relatedness Overview

Try it: https://weblicht.sfs.uni-tuebingen.de/rover/semrel

Semantic relatedness measures

Rover supports six graph-based measures of semantic relatedness between pairs of synsets:

1. Simple Path calculates the length of the path between two synsets via the hypernym relation, relative to the length of the longest such path in GermaNet

2. Wu and Palmer (1994)

3. Leacock and Chodorow (1998)

4. Resnik (1999)

5. Lin (1998)

6. Jiang and Conrath (1997)

Visualize Relatedness

Interactively select a pair of synsets and see the results of relatedness measures:

• search for synsets using the same options and interface as Synset Search
• adjust measures, normalization maximum, and precision

Relatedness results

Once a pair of synsets is selected, Visualize Relatedness displays:

• table of calculation results
• network diagram showing the path(s) between the two synsets via their least common subsumers

Batch Processing

Batch Processing performs relatedness calculations on larger datasets:

• upload a file of up to 200 word pairs
• similar options for adjusting measures, normalization maximum, and precision
• download results in a delimited format for further processing

Thanks!

Try Rover at: https://weblicht.sfs.uni-tuebingen.de/rover/

Send questions, comments, and suggestions for improvement to: germanet-feedback@sfs.uni-tuebingen.de

References

Jiang and Conrath (1997). Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In Proceedings of the 10th Research on Computational Linguistics International Conference, 19–33. Taipei, Taiwan: The Association for Computational Linguistics and Chinese Language Processing

Leacock and Chodorow (1998). ‘Combining Local Context and WordNet Similarity for Word Sense Identification’. In WordNet: An Electronic Lexical Database. MIT Press.

Lin (1998). ‘An Information-Theoretic Definition of Similarity’. In Proceedings of the Fifteenth International Conference on Machine Learning, 296–304. San Francisco, CA, USA: Morgan Kaufmann Publishers.

References (cont.)

Resnik (1999). Semantic Similarity in a Taxonomy: An Information-Based Measure and Its Application to Problems of Ambiguity in Natural Language. Journal of Artificial Intelligence Research 11 (July): 95–130.

Wu and Palmer (1994). Verbs semantics and lexical selection. In Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, 133–138. ACL ’94. Las Cruces, New Mexico: Association for Computational Linguistics.