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Found 11 papers, 1 papers with code
Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis
Abstract Attention based deep learning systems have been demonstrated to be the state of the art approach for aspect-level sentiment analysis, however, end-to-end deep neural networks lack flexibility as one can not easily adjust the network to fix an obvious problem, especially when more training data is not available: e. g. when it always predicts \textit{positive} when seeing the word \textit{disappointed}.
Ranked #17 on
Aspect-Based Sentiment Analysis (ABSA)
on SemEval-2014 Task-4
(Restaurant (Acc) metric)
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification
While sentiment analysis has become an established field in the NLP community, research into languages other than English has been hindered by the lack of resources.
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.
Cross-lingual sentiment classification (CLSC) seeks to use resources from a source language in order to detect sentiment and classify text in a target language.
Adapting Freely Available Resources to Build an Opinion Mining Pipeline in Portuguese
We present a complete UIMA-based pipeline for sentiment analysis in Portuguese news using freely available resources and a minimal set of manually annotated training data.
Automatic Translation of Scientific Documents in the HAL Archive
This paper describes the development of a statistical machine translation system between French and English for scientific papers.
Improving Robustness in Real-World Neural Machine Translation Engines
As a commercial provider of machine translation, we are constantly training engines for a variety of uses, languages, and content types.
