Graph parsing approach to structured sentiment analysis.

Overview

Fine-grained Sentiment Analysis as Dependency Graph Parsing

This repository contains the code and datasets described in following paper: Fine-grained Sentiment Analysis as Dependency Graph Parsing.

Problem description

Fine-grained sentiment analysis can be theoretically cast as an information extraction problem in which one attempts to find all of the opinion tuples $O = O_i,\ldots,O_n$ in a text. Each opinion $O_i$ is a tuple $(h, t, e, p)$

where $h$ is a \textbf {holder} who expresses a \textbf{polarity} $p$ towards a \textbf{target} $t$ through a \textbf{sentiment expression} $e$, implicitly defining the relationships between these elements.

The two examples below (first in English, then in Basque) show the conception of sentiment graphs.

multilingual example

Rather than treating this as a sequence-labeling task, we can treat it as a bilexical dependency graph prediction task, although some decisions must me made. We create two versions (a) head-first and (b) head-final, shown below:

bilexical

Requirements

  1. python3
  2. pytorch
  3. matplotlib
  4. sklearn
  5. gensim
  6. numpy
  7. h5py
  8. transformers
  9. tqdm

Data collection and preprocessing

We provide the preprocessed bilexical sentiment graph data as conllu files in 'data/sent_graphs'. If you want to run the experiments, you can use this data directly. If, however, you are interested in how we create the data, you can use the following steps.

The first step is to download and preprocess the data, and then create the sentiment dependency graphs. The original data can be downloaded and converted to json files using the scripts found at https://github.com/jerbarnes/finegrained_data. After creating the json files for the finegrained datasets following the instructions, you can then place the directories (renamed to 'mpqa', 'ds_unis', 'norec_fine', 'eu', 'ca') in the 'data' directory.

After that, you can use the available scripts to create the bilexical dependency graphs, as mentioned in the paper.

cd data
./create_english_sent_graphs.sh
./create_euca_sent_graphs.sh
./create_norec_sent_graphs
cd ..

Experimental results

To reproduce the results, first you will need to download the word vectors used:

mkdir vectors
cd vectors
wget http://vectors.nlpl.eu/repository/20/58.zip
wget http://vectors.nlpl.eu/repository/20/32.zip
wget http://vectors.nlpl.eu/repository/20/34.zip
wget http://vectors.nlpl.eu/repository/20/18.zip
cd ..

You will similarly need to extract mBERT token representations for all datasets.

./do_bert.sh

Finally, you can run the SLURM scripts to reproduce the experimental results.

./scripts/run_base.sh
./scripts/run_bert.sh
Owner
Jeremy Barnes
I'm a professor of Natural Language Processing. My interests are in multi-linguality and incorporating diverse sources of information into neural networks.
Jeremy Barnes
[Machine Learning Engineer Basic Guide] 부스트캠프 AI Tech - Product Serving 자료

Boostcamp-AI-Tech-Product-Serving 부스트캠프 AI Tech - Product Serving 자료 Repository 구조 part1(MLOps 개론, Model Serving, 머신러닝 프로젝트 라이프 사이클은 별도의 코드가 없으며, part

Sung Yun Byeon 269 Dec 21, 2022
Deep Reinforcement Learning for Multiplayer Online Battle Arena

MOBA_RL Deep Reinforcement Learning for Multiplayer Online Battle Arena Prerequisite Python 3 gym-derk Tensorflow 2.4.1 Dotaservice of TimZaman Seed R

Dohyeong Kim 32 Dec 18, 2022
[ICLR 2021] "Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective" by Wuyang Chen, Xinyu Gong, Zhangyang Wang

Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective [PDF] Wuyang Chen, Xinyu Gong, Zhangyang Wang In ICLR 2

VITA 156 Nov 28, 2022
simple demo codes for Learning to Teach with Dynamic Loss Functions

Learning to Teach with Dynamic Loss Functions This repo contains the simple demo for the NeurIPS-18 paper: Learning to Teach with Dynamic Loss Functio

Lijun Wu 15 Dec 30, 2021
Experiments on continual learning from a stream of pretrained models.

Ex-model CL Ex-model continual learning is a setting where a stream of experts (i.e. model's parameters) is available and a CL model learns from them

Antonio Carta 6 Dec 04, 2022
Real-time pose estimation accelerated with NVIDIA TensorRT

trt_pose Want to detect hand poses? Check out the new trt_pose_hand project for real-time hand pose and gesture recognition! trt_pose is aimed at enab

NVIDIA AI IOT 803 Jan 06, 2023
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

Automatic, Readable, Reusable, Extendable Machin is a reinforcement library designed for pytorch. Build status Platform Status Linux Windows Supported

Iffi 348 Dec 24, 2022
DeepLab2: A TensorFlow Library for Deep Labeling

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks.

Google Research 845 Jan 04, 2023
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course

Deep Learning with TensorFlow 2 and Keras – Notebooks This project accompanies my Deep Learning with TensorFlow 2 and Keras trainings. It contains the

Aurélien Geron 1.9k Dec 15, 2022
PyTorch implementation of Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose

Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose Release Notes The official PyTorch implementation of Neural View S

Angtian Wang 20 Oct 09, 2022
Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)

LUPerson-NL Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL) The repository is for our CVPR2022 paper Large-Scale

43 Dec 26, 2022
Technical Analysis library in pandas for backtesting algotrading and quantitative analysis

bta-lib - A pandas based Technical Analysis Library bta-lib is pandas based technical analysis library and part of the backtrader family. Links Main P

DRo 393 Dec 20, 2022
code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"

code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"

Facebook Research 94 Oct 26, 2022
Normal Learning in Videos with Attention Prototype Network

Codes_APN Official codes of CVPR21 paper: Normal Learning in Videos with Attention Prototype Network (https://arxiv.org/abs/2108.11055) Overview of ou

11 Dec 13, 2022
A package for music online and offline rhythmic information analysis including music Beat, downbeat, tempo and meter tracking.

BeatNet A package for music online and offline rhythmic information analysis including music Beat, downbeat, tempo and meter tracking. This repository

Mojtaba Heydari 157 Dec 27, 2022
Implementation of the paper ''Implicit Feature Refinement for Instance Segmentation''.

Implicit Feature Refinement for Instance Segmentation This repository is an official implementation of the ACM Multimedia 2021 paper Implicit Feature

Lufan Ma 17 Dec 28, 2022
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data

GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data By Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, W

Taihong Xiao 141 Apr 16, 2021
History Aware Multimodal Transformer for Vision-and-Language Navigation

History Aware Multimodal Transformer for Vision-and-Language Navigation This repository is the official implementation of History Aware Multimodal Tra

Shizhe Chen 46 Nov 23, 2022