Comments (2)
Main problem
This paper's main problem is the automation of the analysis of customers' reviews and understanding the reviewers' attitudes to different aspects of a product, such as "price," "service," or "safety." The authors propose a novel embedding refinement method to obtain context-aware embeddings for Targeted aspect-based sentiment analysis (TABSA). This is because of a need for context awareness in previous works, which led to having the same embeddings for words even when the context changes.
Existing work
Attention-based neural networks have demonstrated remarkable progress in the TABSA task, but the authors of the current paper note that the existing approaches usually utilize context-independent or randomly initialized vectors for representing targets and aspects. Therefore, the semantic information is lost, and the interdependence among specific targets, corresponding aspects, and context, is not considered.
Inputs
- A collection of sentences
Outputs
- Aspects and Sentiments
Example
The goal of TABSA is that, given an input sentence, we want to extract the sentiment of the aspect that belongs to a target.
For example, "location1 is your best bet for secure although expensive and location2 is too far."
Target: location1, Aspect: SAFETY, Sentiment: Positive
Target: location1, Aspect: PRICE, Sentiment: Negative
Target: location2, Aspect: TRANSIT, Sentiment: Negative
Proposed Method
They present a novel embedding refinement approach to obtain context-aware embeddings for the TABSA task rather than context-independent or randomly initialized embeddings:
- A sparse coefficient vector is leveraged to select highly correlated words from the sentence.
- The representations of target and aspect are adjusted to make these highly-correlated words more valuable.
- The aspect embedding is fine-tuned to be closer to the highly correlated target and further away from the irrelevant targets.
The model framework has the following steps, which are provided as a schema in the figure after the steps:
- Sentence embedding matrix X is fed into the fully connected layer and step function to create sparse coefficient vector u'.
- The hidden output of u' is used to refine the target and aspect embeddings.
- Compute the squared Euclidean function and train the model to minimize the distance to obtain the final refined embeddings for the target and aspect.
Experimental Setup
Dataset
- SentiHood: annotated sentences containing one or two location target mentions.
- Task 12 of Semeval 2015": removing sentences containing no targets or NULL targets.
They use Glove to initialize the word embeddings in experiments.
Evaluation and Metrics
They use the metrics below:
- Macro average F1, Strict accuracy (Acc.), and AUC for aspect detection
- Acc. and AUC for sentiment classification
Baselines
- LSTM-Final. Bidirectional LSTM that only uses the final hidden states
- LSTM-Loc. Bidirectional LSTM that uses the hidden states where the location target is.
- SenticLSTM. Bidirectional LSTM that uses external knowledge.
- Delayed-Memory. Delayed memory mechanism
Results
The experimental results show that incorporating context-aware embeddings of targets and aspects into the neural models improves:
- Aspect detection by 2.9% in strict accuracy
- Sentiment classification by 1.8% in strict accuracy
Code
https://github.com/BinLiang-NLP/CAER-TABSA (Official)
Presentation
No presentation was provided.
Criticism
Not considering latent or implicit aspects.
from lady.
@farinamhz nice summary. thanks.
from lady.
Related Issues (20)
- Adding HAST as a supervised baseline HOT 10
- Classification baseline for aspect term extraction HOT 18
- pipeline progress flow
- Check the existing readme and codeline HOT 2
- Gif image/video for illustrating the pipeline HOT 2
- Dockerize and fix installation on linux HOT 13
- a server for the web app
- Setup and Quickstart HOT 5
- Aspect based sentiment analysis + Running Bert and Cat library
- Adding Twitter Reviews Dataset HOT 1
- Needing for update in OCTIS library HOT 2
- Aspect Sentiment Triplet Extraction Baseline HOT 20
- New baseline for Aspect-Based Sentiment Analysis HOT 2
- Literature Review on Aspect and Sentiment Extraction HOT 1
- Adding a new tanslation model to the pipeline HOT 1
- OCTIS.CTM throws a value error during the training phase HOT 3
- Updating stats on quality of translation HOT 4
- Incorporating Underrepresented Languages: A Focus on Low-Resource Languages HOT 1
- Dataset for LADy (Story Board) HOT 22
- LADy Progress Documentation HOT 16
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from lady.