Aspect Extraction
32 papers with code • 6 benchmarks • 4 datasets
Aspect extraction is the task of identifying and extracting terms relevant for opinion mining and sentiment analysis, for example terms for product attributes or features.
Latest papers
Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble Approaches
Aspect-based Sentiment Analysis (ABSA) is a task whose objective is to classify the individual sentiment polarity of all entities, called aspects, in a sentence.
Automatic Aspect Extraction from Scientific Texts
Being able to extract from scientific papers their main points, key insights, and other important information, referred to here as aspects, might facilitate the process of conducting a scientific literature review.
InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis
We introduce InstructABSA, an instruction learning paradigm for Aspect-Based Sentiment Analysis (ABSA) subtasks.
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs
The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text.
A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis
Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification).
SETSum: Summarization and Visualization of Student Evaluations of Teaching
Ten university professors from diverse departments serve as evaluators of the system and all agree that SETSum helps them interpret SET results more efficiently; and 6 out of 10 instructors prefer our system over the standard static PDF report (while the remaining 4 would like to have both).
Automatic Controllable Product Copywriting for E-Commerce
Automatic product description generation for e-commerce has witnessed significant advancement in the past decade.
Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction
There has been significant progress in utilizing weakly supervised approaches, which require only a small set of seed words for training aspect classifiers.
Improving Aspect Extraction based on Rules through Deep Syntax-Semantics Communication
Recent studies show integrating language resources which consist of lexical resources, syntactic resources and semantic resources can improve the performance of natural language processing (NLP) tasks.
DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect Extraction
We name this scheme DILBERT: Domain Invariant Learning with BERT, and customize it for aspect extraction in the unsupervised domain adaptation setting.