Publication | Closed Access
Complementing global and local contexts in representing API descriptions to improve API retrieval tasks
20
Citations
48
References
2018
Year
Unknown Venue
EngineeringSemantic SearchSemanticsSemantic WebCorpus LinguisticsText MiningOpen ApiWord EmbeddingsNatural Language ProcessingInformation RetrievalData ScienceApi DescriptionsSemantic ApproachComputational LinguisticsLanguage StudiesApi ElementsMachine TranslationKnowledge DiscoveryWord2vec VectorsDistributional SemanticsLocal ContextsVector Space ModelApi DocumentationApi Retrieval TasksLinguisticsSemantic SimilaritySemantic Representation
When being trained on API documentation and tutorials, Word2vec produces vector representations to estimate the relevance between texts and API elements. However, existing Word2vec-based approaches to measure document similarities aggregate Word2vec vectors of individual words or APIs to build the representation of a document as if the words are independent. Thus, the semantics of API descriptions or code fragments are not well represented.
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