COVER

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About COVER

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COVER is a lexical resource providing common-sense knowledge on nouns, and it is automatically generated by combining the lexicographic precision propert to BabelNet and the common-sense hosted in ConceptNet. The reference publication for the resource is [bibcite key=mensa2018cover].

The input for the algorithm generating COVER is obtained via the ClOseSt system.[/vc_column_text][vc_tta_accordion active_section=”0″ collapsible_all=”true”][vc_tta_section title=”Learn about the COVER structure” tab_id=”1509333782364-cd6bdf58-f37c”][vc_column_text]COVER is a json textual file that contains a series of vectors.

Each vector (exemplar) is organized in five fields, namely:

  • exemplar_label that is the name of the vector (obtained by the input given to the system);
  • exemplar_bsi that is the BabelNet synset ID for the exemplar;
  • exemplar_wnsi that is the WordNet synset ID for the exemplar (set to wn:00000000n if the vector has no WordNet synset ID);
  • exemplar_synsetTerms that is the list of lexicalization for the synset representing the exemplar;
  • exemplar_content that lists the values of the exemplar, organized by dimension.

Each dimension is then organized as follows:

  • dim_name that represents the name of the dimension (the ConceptNet relationship);
  • dim_bsi that represents the BabelNet synset ID of the dimension (it works like an unique identifier for the dimension);
  • dim_values that lists all the values found for that dimension.

Finally, each value (concept) of a dimension is organized in:

  • value_wikititle that represents the Wikipedia Title name for the value concept at hand (set to empty string if the concept has no Wikipedia Title) ;
  • value_bsi that represents the BabelNet synset ID for the concept;
  • value_wnsi that represents the WordNet synset ID for the concept (set to wn:00000000n if the concept has no WordNet synset ID)
  • value_terms that lists the lexicalizations for the concept.

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Version 2.1

The version 2.1 relies on the same algorithm adopted in version 2.0, but is built on a much wider input  (31K concepts against the 16K of the version 2.0).

Publication date

27 April 2017

Complete list of relationships
RelatedTo, Synonym, IsA, HasContext, Antonym, FormOf, DerivedFrom, AtLocation, HasA, PartOf, SimilarTo, UsedFor, HasProperty, Causes, CapableOf, HasPrerequisite, HasSubevent, MadeOf, Desires, DBP_Genre, MotivatedByGoal, CreatedBy, Entails, CausesDesire, NotDesires, ReceivesAction, InstanceOf, NotHasProperty, influencedBy, HasLastSubevent, DefinedAs, HasFirstSubevent, DBP_Field, LocatedNear, DBP_KnownFor, NotCapableOf
Statistics about this version

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Version ABS-COVER

The Version 2.1 is has also been enriched with abstractness values for each value provided by the exemplars. Details on this version can be found in [bibcite key=mensa18grasping].

Publication date

01 May 2018

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Version 2.0

The version 2.0 of COVER relies on a hugely improved algorithm.

Specifically:

  • The number of  input concepts has been extended.
  • The new version of ConceptNet (5.5.0) has been adopted.
  • New mechanism for the identification of extracted terms (relies on NASARI Embedded).
  • New threshold of similarity for the extracted terms (set to 0.6).
Publication date

01 April 2017

 

Version 1.1

The version 1.1 of COVER is based on the same algorithm adopted in version 1.0, but is built upon a slightly bigger input.

Detailed information about the resource can be found in [bibcite key=mensa17ttcse].

Publication date

02 February 2017

 

Version 1.0

The version 1.0 of COVER relies on a new algorithm. Specifically, the NASARI autoinjection process was added: vectors that would come out empty from the process are now kept in the final resource. They now contain elements from the correspondent NASARI vector, that are injected into the RelatedTo dimension.

Publication date

31 January 2017

 

Version 0.9

The version 0.9 of COVER is the first stable version of the resource.

The resource has been employed in the SemEval 2017 competition task[bibcite key=mensa17merali].

Publication date

30 January 2017

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Creative Commons License All of the above data is licensed under a Creative Commons Attribution 3.0 United States License.

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Reference papers

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