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Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC.
Ranked #9 on Aspect-Based Sentiment Analysis on SemEval 2014 Task 4 Sub Task 2 (using extra training data)
Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect extraction) and the sentiment polarities (sentiment detection) towards them.
Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings.
Ranked #3 on Aspect Extraction on SemEval 2015 Task 12
We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e. g., in the form of product domain labels and user-provided ratings).
We introduce a hybrid technique which combines machine learning and rule based model.
Multilingual sequence labeling is a task of predicting label sequences using a single unified model for multiple languages.
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE).
Ranked #4 on Aspect Sentiment Triplet Extraction on SemEval
Aspect-Based Sentiment Analysis (ABSA) studies the consumer opinion on the market products.
Ranked #1 on Aspect Extraction on SemEval 2014 Task 4 Sub Task 2