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Found 15 papers, 2 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
Cross-lingual Emotion Intensity Prediction
Consequently, we explore cross-lingual transfer approaches for fine-grained emotion detection in Spanish and Catalan tweets.
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.
The NewSoMe Corpus: A Unifying Opinion Annotation Framework across Genres and in Multiple Languages
We present the NewSoMe (News and Social Media) Corpus, a set of subcorpora with annotations on opinion expressions across genres (news reports, blogs, product reviews and tweets) and covering multiple languages (English, Spanish, Catalan and Portuguese).
On the Effect of Word Order on Cross-lingual Sentiment Analysis
Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent neural networks (RNNs) or convolutional networks (CNNs).
Cross-Lingual Sentiment Classification
General Classification
+4
Evaluating morphological typology in zero-shot cross-lingual transfer
Furthermore, POS tagging is more sensitive to morphological typology than sentiment analysis and, on this task, models perform much better on fusional languages than on the other typologies.
PosEdiOn: Post-Editing Assessment in PythOn
There is currently an extended use of post-editing of machine translation (PEMT) in the translation industry.
Quantitative Analysis of Post-Editing Effort Indicators for NMT
The recent improvements in machine translation (MT) have boosted the use of post-editing (PE) in the translation industry.
Spanish TimeBank 1.0
The Spanish TimeBank Corpus contains 210 documents, which have been annotated with time and eventuality information according to the TimeML scheme, now accepted as an international cross-language ISO standard
Catalan TimeBank 1.0
The Catalan TimeBank Corpus contains 210 documents (mostly news reports), which have been annotated with time and eventuality information according to the TimeML scheme (Pustejovsky et al., 2005), now accepted as an international cross-language ISO standard
