As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis
(ABSA), aiming to analyze and understand people's opinions at the aspect level, has been …

Data augmentation is an effective solution to data scarcity in low-resource scenarios.
However, when applied to token-level tasks such as NER, data augmentation methods often …

Abstract Named Entity Recognition (NER) for low-resource languages is a both practical and
challenging research problem. This paper addresses zero-shot transfer for cross-lingual …

Cross-lingual named entity recognition (CrossNER) faces challenges stemming from
uneven performance due to the scarcity of multilingual corpora, especially for non-English …

Many efforts have been made in solving the Aspect-based sentiment analysis (ABSA) task.
While most existing studies focus on English texts, handling ABSA in resource-poor …

In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the
linguistic gap by training on pseudo-labeled target-language data. However, due to sub …

Cross-lingual named entity recognition (NER) suffers from data scarcity in the target
languages, especially under zero-shot settings. Existing translate-train or knowledge …

We study the problem of query attribute value extraction, which aims to identify named
entities from user queries as diverse surface form attribute values and afterward transform …

Numerous attempts have been made to address Aspect-based Sentiment Analysis (ABSA),
with a predominant emphasis on English texts. Tackling ABSA in low-resource languages …

Aspect-based sentiment analysis (ABSA), an extensively researched area in the field of
natural language processing (NLP), predicts the sentiment expressed in a text relative to the …