Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a "target" domain when the only available training data belongs to a different "source" domain. In this extended abstract we briefly describe a new DA method called Distributional Correspondence Indexing (DCI) for sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. The experiments we have conducted show that DCI obtains better performance than current state-of-the-art techniques for cross-lingual and cross-domain sentiment classification.
Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification (Extended Abstract)
Moreo Fernandez A;Esuli A;Sebastiani F
2018
Abstract
Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a "target" domain when the only available training data belongs to a different "source" domain. In this extended abstract we briefly describe a new DA method called Distributional Correspondence Indexing (DCI) for sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. The experiments we have conducted show that DCI obtains better performance than current state-of-the-art techniques for cross-lingual and cross-domain sentiment classification.File | Dimensione | Formato | |
---|---|---|---|
prod_401236-doc_140850.pdf
accesso aperto
Descrizione: Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification (Extended Abstract)
Tipologia:
Versione Editoriale (PDF)
Dimensione
3.11 MB
Formato
Adobe PDF
|
3.11 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.