Crosslingual Sentiment Analysis

Expensive feature engineering based on WordNet senses has been shown be useful for document level sentimentto classification. A plausible reason for such a performance improvement is the reduction in data …...

Cross-Lingual Learning provides a mech- anism to adapt NLP tools available for la- bel rich languages to achieve similar tasks for label-scarce languages. An efficient cross-lingual tool significantly reduces the …...

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. Although much research in …...

AbstractWord embeddings learned in two languages can be mapped to a common space to produce Bilingual Word Embeddings (BWE). Unsupervised BWE methods learn such a mapping without any parallel data. …...

AbstractSentiment analysis in low-resource languages suffers from the lack of training data. Cross-lingual sentiment analysis (CLSA) aims to improve the performance on these languages by leveraging annotated data from other …...

AbstractThe combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages …...

AbstractExisting sentiment analysis models have achieved great advances with the help of sufficient sentiment annotations. Unfortunately, many languages do not have sufficient sentiment corpus. To this end, recent studies have …...