Paper Info
Title
Aspect-Level Cross-Lingual Sentiment Classification With Constrained Smt
Abstract
Most cross-lingual sentiment classification (CLSC) research so far has been performed at sentence or document level. Aspect-level CLSC, which is more appropriate for many applications, presents the additional difficulty that we consider sub-sentential opinionated units which have to be mapped across languages. In this paper, we extend the possible cross-lingual sentiment analysis settings to aspect-level specific use cases. We propose a method, based on constrained SMT, to transfer opinionated units across languages by preserving their boundaries. We show that cross-language sentiment classifiers built with this method achieve comparable results to monolingual ones, and we compare different cross-lingual settings.
Year
Venue
Field
2015
PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2
Cross lingual,Use case,Sentiment analysis,Computer science,Artificial intelligence,Natural language processing,Sentence
DocType
Volume
Citations 
Conference
P15-2
3
PageRank 
References 
Authors
0.52
23
1
Name
Order
Citations
PageRank
Patrik Lambert127723.36