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

Related tags

Deep Learning information-extraction keyword-extraction keyphrase-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]

    • You might also like...
    Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

    Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

    Dongkyu Lee 4 Sep 18, 2022
    Code & Data for Enhancing Photorealism Enhancement
    Code & Data for Enhancing Photorealism Enhancement

    Code & Data for Enhancing Photorealism Enhancement

    Intel ISL (Intel Intelligent Systems Lab) 1.1k Jan 8, 2023
    This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their coordinates and detected labels.
    This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their coordinates and detected labels.

    This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their

    Liron Bdolah 8 May 22, 2022
    Source code for paper "Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling", AAAI 2021

    ATLOP Code for AAAI 2021 paper Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling. If you make use of this co

    Wenxuan Zhou 146 Nov 29, 2022
    ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration
    ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration

    ROSITA News & Updates (24/08/2021) Release the demo to perform fine-grained semantic alignments using the pretrained ROSITA model. (15/08/2021) Releas

    Vision and Language Group@ MIL 48 Dec 23, 2022
    This is the open-source reference implementation of the SIGGRAPH 2021 paper Intersection-free Rigid Body Dynamics.
    This is the open-source reference implementation of the SIGGRAPH 2021 paper Intersection-free Rigid Body Dynamics.

    Robust, intersection-free, simulations of rigid bodies.

    null 93 Dec 23, 2022
    Character-Input - Create a program that asks the user to enter their name and their age

    Character-Input Create a program that asks the user to enter their name and thei

    PyLaboratory 0 Feb 6, 2022
    Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.
    Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.

    How The New York Times can increase Engagement on Facebook Using machine learning to understand characteristics of news content that garners "high" Fa

    Jessica Miles 0 Sep 16, 2021
    MEDS: Enhancing Memory Error Detection for Large-Scale Applications

    MEDS: Enhancing Memory Error Detection for Large-Scale Applications Prerequisites cmake and clang Build MEDS supporting compiler $ make Build Using Do

    Secomp Lab at Purdue University 34 Dec 14, 2022
    Owner
    Professor at iSchool of Nanjing University of Science and Technology
    null
    GitHub
    PyTorch implementation of CVPR 2020 paper (Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence) and pre-trained model on ImageNet dataset

    Reference-Based-Sketch-Image-Colorization-ImageNet This is a PyTorch implementation of CVPR 2020 paper (Reference-Based Sketch Image Colorization usin

    Yuzhi ZHAO 11 Jul 28, 2022
    [AAAI2021] The source code for our paper 《Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion》.

    DSM The source code for paper Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion Project Website; Datasets li

    Jinpeng Wang 114 Oct 16, 2022
    This repository contains numerical implementation for the paper Intertemporal Pricing under Reference Effects: Integrating Reference Effects and Consumer Heterogeneity.

    This repository contains numerical implementation for the paper Intertemporal Pricing under Reference Effects: Integrating Reference Effects and Consumer Heterogeneity.

    Hansheng Jiang 6 Nov 18, 2022
    Source code for paper "ATP: AMRize Than Parse! Enhancing AMR Parsing with PseudoAMRs" @NAACL-2022

    ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs Hi this is the source code of our paper "ATP: AMRize Then Parse! Enhancing AMR Parsing w

    Chen Liang 13 Nov 23, 2022
    This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations at CVPR'21. According to some product reasons, we are not planning to release the training/testing codes and models. However, we will release the dataset and the scripts to prepare the dataset.

    TransFill-Reference-Inpainting This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transf

    Yuqian Zhou 80 Dec 8, 2022
    This is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.

    SLATE This is the official source code for SLATE. We provide the code for the model, the training code and a dataset loader for the 3D Shapes dataset.

    Gautam Singh 66 Dec 26, 2022
    Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.

    This repository is a toolkit to do machine learning for programming languages. It implements tokenization, dataset preprocessing, model training and m

    Facebook Research 408 Jan 1, 2023
    Wanli Li and Tieyun Qian: Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction, IJCNN 2021

    MRefG Wanli Li and Tieyun Qian: "Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction", IJCNN 2021 1. Requirements To reproduc

    万理 5 Jul 26, 2022
    Code repo for EMNLP21 paper "Zero-Shot Information Extraction as a Unified Text-to-Triple Translation"

    Zero-Shot Information Extraction as a Unified Text-to-Triple Translation Source code repo for paper Zero-Shot Information Extraction as a Unified Text

    cgraywang 88 Dec 31, 2022
    Implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

    SemCo The official pytorch implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

    null 42 Nov 14, 2022