Dataset and Source code of paper 'Enhancing Keyphrase Extraction from Academic Articles with their Reference Information'.

Related tags

Deep Learninginformation-extractionkeyword-extractionkeyphrase-extraction
Overview

Enhancing Keyphrase Extraction from Academic Articles with their Reference Information

Overview

Dataset and code for paper "Enhancing Keyphrase Extraction from Academic Articles with their Reference Information".

The research content of this project is to analyze the impact of the introduction of reference title in scientific literature on the effect of keyword extraction. This project uses three datasets: SemEval-2010, PubMed and LIS-2000, which are located in the dataset folder. At the same time, we use two unsupervised methods: TF-IDF and TextRank, and three supervised learning methods: NaiveBayes, CRF and BiLSTM-CRF. The first four are traditional keywords extraction methods, located in the folder ML, and the last one is deep learning method, located in the folder DL.

Directory structure

Keyphrase_Extraction:                 Root directory
│  dl.bat:                            Batch commands to run deep learning model
│  ml.bat:                            Batch commands to run traditional models
│ 
├─Dataset:                            Store experimental datasets
│      SemEval-2010:                  Contains 244 scientific papers 
│      PubMed:                        Contains 1316 scientific papers
│      LIS-2000:                      Contains 2000 scientific papers
│ 
├─DL:                                 Store the source code of the deep learning model
│  │  build_path.py:                  Create file paths for saving preprocessed data
│  │  crf.py:                         Source code of CRF algorithm implementation(Use pytorch framework)
│  │  main.py:                        The main function of running the program
│  │  model.py:                       Source code of BiLSTM-CRF model
│  │  preprocess.py:                  Source code of preprocessing function
│  │  textrank.py:                    Source code of TextRank algorithm implementation.
│  │  tf_idf.py:                      Source code of TF-IDF algorithm implementation.
│  │  utils.py:                       Some auxiliary functions
│  ├─models:                          Parameter configuration of deep learning models
│  └─datas
│        tags:                        Label settings for sequence labeling
│ 
└─ML:                                 Store the source code of the traditional models
    │  build_path.py:                 Create file paths for saving preprocessed data
    │  configs.py:                    Path configuration file
    │  crf.py:                        Source code of CRF algorithm implementation(Use CRF++ Toolkit)
    │  evaluate.py:                   Source code for result evaluation
    │  naivebayes.py:                 Source code of naivebayes algorithm implementation(Use KEA-3.0 Toolkit)
    │  preprocessing.py:              Source code of preprocessing function
    │  textrank.py:                   Source code of TextRank algorithm implementation
    │  tf_idf.py:                     Source code of TF-IDF algorithm implementation
    │  utils.py:                      Some auxiliary functions
    ├─CRF++:                          CRF++ Toolkit
    └─KEA-3.0:                        KEA-3.0 Toolkit

Dataset Description

The dataset includes the following three json files:

  • SemEval-2010: SemEval-2010 Task 5 dataset, it contains 244 scientific papers and can be visited at: https://semeval2.fbk.eu/semeval2.php?location=data.
  • PubMed: Contains 1316 scientific papers from PubMed (https://github.com/boudinfl/ake-datasets/tree/master/datasets/PubMed).
  • LIS-2000: Contains 2000 scientific papers from journals in Library and Information Science (LIS).

    Each line of the json file includes:

  • title (T): The title of the paper.
  • abstract (A): The abstract of the paper.
  • introduction (I): The introduction of the paper.
  • conclusion (C): The conclusion of the paper.
  • body1 (Fp): The first sentence of each paragraph.
  • body2 (Lp): The last sentence of each paragraph.
  • full_text (F): The full text of the paper.
  • references (R): references list and only the title of each reference is provided.
  • keywords (K): the keywords of the paper and these keywords were annotated manually.

    Quick Start

    In order to facilitate the reproduction of the experimental results, the project uses bat batch command to run the program uniformly (only in Windows Environment). The dl.bat file is the batch command to run the deep learning model, and the ml.bat file is the batch command to run the traditional algorithm.

    How does it work?

    In the Windows environment, use the key combination Win + R and enter cmd to open the DOS command box, and switch to the project's root directory (Keyphrase_Extraction). Then input dl.bat, that is, run deep learning model to get the result of keyword extraction; Enter ml.bat to run traditional algorithm to get keywords Extract the results.

    Experimental results

    The following figures show that the influence of reference information on keyphrase extraction results of TF*IDF, TextRank, NB, CRF and BiLSTM-CRF.

    Table 1: Keyphrase extraction performance of multiple corpora constructed using different logical structure texts on the dataset of SemEval-2010 Table1

    Table 2: Keyphrase extraction performance of multiple corpora constructed using different logical structure texts on the dataset of PubMed Table2

    Table 3: Keyphrase extraction performance of multiple corpora constructed using different logical structure texts on the dataset of LIS-2000 Table3

    Note: The yellow, green and blue bold fonts in the table represent the largest of the P, R and F1 value obtained from different corpora using the same model, respectively.

    Dependency packages

    Before running this project, check that the following Python packages are included in your runtime environment.

  • pytorch 1.7.1
  • nltk 3.5
  • numpy 1.19.2
  • pandas 1.1.3
  • tqdm 4.50.2

    Citation

    Please cite the following paper if you use this codes and dataset in your work.

    Chengzhi Zhang, Lei Zhao, Mengyuan Zhao, Yingyi Zhang. Enhancing Keyphrase Extraction from Academic Articles with their Reference Information. Scientometrics, 2021. (in press) [arXiv]

    • Owner
    Professor at iSchool of Nanjing University of Science and Technology
    GitHub Repository
    How to Predict Stock Prices Easily Demo

    How-to-Predict-Stock-Prices-Easily-Demo How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube ##Overview This is th

    Siraj Raval 752 Nov 16, 2022
    Example of a Quantum LSTM

    Example of a Quantum LSTM

    Riccardo Di Sipio 36 Oct 31, 2022
    TensorFlow implementation of original paper : https://github.com/hszhao/PSPNet

    Keras implementation of PSPNet(caffe) Implemented Architecture of Pyramid Scene Parsing Network in Keras. For the best compability please use Python3.

    VladKry 386 Dec 29, 2022
    Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

    Hurdles to Progress in Long-form Question Answering This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hur

    Kalpesh Krishna 41 Nov 08, 2022
    TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

    TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? Source: Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize

    Aritra Roy Gosthipaty 23 Dec 24, 2022
    A Tensorflow implementation of BicycleGAN.

    BicycleGAN implementation in Tensorflow As part of the implementation series of Joseph Lim's group at USC, our motivation is to accelerate (or sometim

    Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 97 Dec 02, 2022
    Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"

    Medical Image Segmentation with Guided Attention This repository contains the code of our paper: "'Multi-scale self-guided attention for medical image

    Ashish Sinha 394 Dec 28, 2022
    Mixup for Supervision, Semi- and Self-Supervision Learning Toolbox and Benchmark

    OpenSelfSup News Downstream tasks now support more methods(Mask RCNN-FPN, RetinaNet, Keypoints RCNN) and more datasets(Cityscapes). 'GaussianBlur' is

    AI Lab, Westlake University 332 Jan 03, 2023
    Official implementation of ACTION-Net: Multipath Excitation for Action Recognition (CVPR'21).

    ACTION-Net Official implementation of ACTION-Net: Multipath Excitation for Action Recognition (CVPR'21). Getting Started EgoGesture data folder struct

    V-Sense 171 Dec 26, 2022
    [ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages

    Discriminative Region-based Multi-Label Zero-Shot Learning (ICCV 2021) [arXiv][Project page coming soon] Sanath Narayan*, Akshita Gupta*, Salman Kh

    Akshita Gupta 54 Nov 21, 2022
    Attentive Implicit Representation Networks (AIR-Nets)

    Attentive Implicit Representation Networks (AIR-Nets) Preprint | Supplementary | Accepted at the International Conference on 3D Vision (3DV) teaser.mo

    29 Dec 07, 2022
    PyTorch implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".

    pix2pix-pytorch PyTorch implementation of Image-to-Image Translation Using Conditional Adversarial Networks. Based on pix2pix by Phillip Isola et al.

    mrzhu 383 Dec 17, 2022
    Official repository for Natural Image Matting via Guided Contextual Attention

    GCA-Matting: Natural Image Matting via Guided Contextual Attention The source codes and models of Natural Image Matting via Guided Contextual Attentio

    Li Yaoyi 349 Dec 26, 2022
    Free course that takes you from zero to Reinforcement Learning PRO 🦸🏻‍🦸🏽

    The Hands-on Reinforcement Learning course 🚀 From zero to HERO 🦸🏻‍🦸🏽 Out of intense complexities, intense simplicities emerge. -- Winston Churchi

    Pau Labarta Bajo 260 Dec 28, 2022
    Real-time Object Detection for Streaming Perception, CVPR 2022

    StreamYOLO Real-time Object Detection for Streaming Perception Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li, Sun Jian Real-time Object Detection

    Jinrong Yang 237 Dec 27, 2022
    Keras-1D-ACGAN-Data-Augmentation

    Keras-1D-ACGAN-Data-Augmentation What is the ACGAN(Auxiliary Classifier GANs) ? Related Paper : [Abstract : Synthesizing high resolution photorealisti

    Jae-Hoon Shim 7 Dec 23, 2022
    Official codebase for ICLR oral paper Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling

    CLIORA This is the official codebase for ICLR oral paper: Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling. We introduce

    Bo Wan 32 Dec 23, 2022
    Repository for MDPGT

    MD-PGT Repository for implementing and reproducing the results for the paper MDPGT: Momentum-based Decentralized Policy Gradient Tracking. Available E

    Xian Yeow Lee 2 Dec 30, 2021
    Leveraging Two Types of Global Graph for Sequential Fashion Recommendation, ICMR 2021

    This is the repo for the paper: Leveraging Two Types of Global Graph for Sequential Fashion Recommendation Requirements OS: Ubuntu 16.04 or higher ver

    Yujuan Ding 10 Oct 10, 2022
    Unofficial implementation (replicates paper results!) of MINER: Multiscale Implicit Neural Representations in pytorch-lightning

    MINER_pl Unofficial implementation of MINER: Multiscale Implicit Neural Representations in pytorch-lightning. 📖 Ref readings Laplacian pyramid explan

    AI葵 51 Nov 28, 2022