VerbNet is the largest on-line network of English verbs that links their syntactic and semantic patterns. It is a hierarchical, domain-independent, broad-coverage verb lexicon with mappings to other lexical resources.

How is VerbNet organized?

VerbNet is organized into verb classes extending Levin (1993) classes through refinement and addition of subclasses to achieve syntactic and semantic coherence among members of a class.

What information does each verb class contain?

Each verb class in VN is completely described by thematic roles, selectional preferences of the arguments, and frames consisting of a syntactic description and a semantic representation with subevent structure.

What are syntactic frames in VerbNet?

Each VN class contains a set of syntactic descriptions, or syntactic frames, depicting the possible surface realizations of the argument structure for constructions such as transitive, intransitive, prepositional phrases, resultatives, and a large set of diathesis alternations.

What are semantic predicates in VerbNet?

Each frame is associated with explicit semantic information, expressed as a conjunction of boolean semantic predicates such as `motion,' `contact,' or `cause.' Each semantic predicate is associated with an event variable E that allows predicates to specify when in the event the predicate is true.

How does VerbNet integrate with other lexical resources?

VerbNet has mappings to other lexical resources, such as WordNet, PropBank, and FrameNet, facilitating interoperability and knowledge sharing.

VerbNet

VerbNet | Find AI List

Use Cases

Semantic Role Labeling

Identifying the semantic roles of arguments in a sentence.

VIEW EXECUTION STEPS

Input sentence into a syntactic parser.

Identify the main verb in the sentence.

Lookup the verb in VerbNet to find its class.

Use the VerbNet frame to determine possible thematic roles for the arguments.

Assign the most likely thematic role to each argument based on syntactic and semantic restrictions.

Machine Translation

Improving the accuracy of machine translation by ensuring that verbs are translated with the correct semantic roles.

VIEW EXECUTION STEPS

Parse the source sentence and identify the main verb.

Lookup the verb in VerbNet to identify its semantic class.

Translate the verb and its arguments into the target language.

Ensure that the translated verb and arguments maintain the same thematic roles as the source sentence.

Generate the translated sentence.

Text Summarization

Extracting the most important information from a text by identifying the key verbs and their arguments.

VIEW EXECUTION STEPS

Parse the text and identify all the verbs.

Lookup each verb in VerbNet to determine its importance based on its class.

Extract the arguments associated with the most important verbs.

Summarize the text by including the most important verbs and their arguments.

Generate the summary.

Question Answering

Answering questions by understanding the semantic relationships between verbs and their arguments.

VIEW EXECUTION STEPS

Parse the question and identify the main verb.

Lookup the verb in VerbNet to find its class and thematic roles.

Parse the context text and identify sentences containing the same verb or verbs from the same class.

Extract the arguments associated with the verb in the context text.

Match the arguments to the thematic roles in the question to find the answer.

Information Extraction

Extracting structured information from unstructured text by identifying verbs and their arguments.

VIEW EXECUTION STEPS

Parse the text and identify all the verbs.

Lookup each verb in VerbNet to determine its class and thematic roles.

Extract the arguments associated with each verb.

Store the extracted information in a structured format (e.g., a database or knowledge graph).

Use the structured information for data analysis or decision-making.

VerbNet

About VerbNet

Core Capabilities

Main Tasks

Semantic Role Labeling

What this tool is best suited for

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Key Features

Hierarchical Verb Classes

Syntactic Frames

Semantic Predicates

Thematic Roles

Integration with Lexical Resources

Use Cases

Semantic Role Labeling

Machine Translation

Text Summarization

Question Answering

Information Extraction

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