Five_kwextraction.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ali-MH-Mansour/Keyword-Extraction/blob/main/Five_kwextraction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "title = \"VECTORIZATION OF TEXT USING DATA MINING METHODS\"\n",
        "text = \"In the text mining tasks, textual representation should be not only efficient but also interpretable, as this enables an understanding of the operational logic underlying the data mining models. Traditional text vectorization methods such as TF-IDF and bag-of-words are effective and characterized by intuitive interpretability, but suffer from the «curse of dimensionality», and they are unable to capture the meanings of words. On the other hand, modern distributed methods effectively capture the hidden semantics, but they are computationally intensive, time-consuming, and uninterpretable. This article proposes a new text vectorization method called Bag of weighted Concepts BoWC that presents a document according to the concepts’ information it contains. The proposed method creates concepts by clustering word vectors (i.e. word embedding) then uses the frequencies of these concept clusters to represent document vectors. To enrich the resulted document representation, a new modified weighting function is proposed for weighting concepts based on statistics extracted from word embedding information. The generated vectors are characterized by interpretability, low dimensionality, high accuracy, and low computational costs when used in data mining tasks. The proposed method has been tested on five different benchmark datasets in two data mining tasks; document clustering and classification, and compared with several baselines, including Bag-of-words, TF-IDF, Averaged GloVe, Bag-of-Concepts, and VLAC. The results indicate that BoWC outperforms most baselines and gives 7% better accuracy on average\"\n",
        "full_text = title +\", \"+ text \n",
        "print(\"The whole text to be used\\n\",full_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8T09_fszzjf",
        "outputId": "b9bce4eb-d8a3-4690-9e44-907816adac88"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The whole text to be used\n",
            " VECTORIZATION OF TEXT USING DATA MINING METHODS, In the text mining tasks, textual representation should be not only efficient but also interpretable, as this enables an understanding of the operational logic underlying the data mining models. Traditional text vectorization methods such as TF-IDF and bag-of-words are effective and characterized by intuitive interpretability, but suffer from the «curse of dimensionality», and they are unable to capture the meanings of words. On the other hand, modern distributed methods effectively capture the hidden semantics, but they are computationally intensive, time-consuming, and uninterpretable. This article proposes a new text vectorization method called Bag of weighted Concepts BoWC that presents a document according to the concepts’ information it contains. The proposed method creates concepts by clustering word vectors (i.e. word embedding) then uses the frequencies of these concept clusters to represent document vectors. To enrich the resulted document representation, a new modified weighting function is proposed for weighting concepts based on statistics extracted from word embedding information. The generated vectors are characterized by interpretability, low dimensionality, high accuracy, and low computational costs when used in data mining tasks. The proposed method has been tested on five different benchmark datasets in two data mining tasks; document clustering and classification, and compared with several baselines, including Bag-of-words, TF-IDF, Averaged GloVe, Bag-of-Concepts, and VLAC. The results indicate that BoWC outperforms most baselines and gives 7% better accuracy on average\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Rake"
      ],
      "metadata": {
        "id": "bHnBSb4Ez_t7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install multi_rake"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "6SUyhf2Pz6Me",
        "outputId": "c4a254f6-597e-4aaf-ae08-56e99736e4d5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rake-nltk\n",
            "  Downloading rake_nltk-1.0.6-py3-none-any.whl (9.1 kB)\n",
            "Collecting nltk<4.0.0,>=3.6.2\n",
            "  Downloading nltk-3.6.7-py3-none-any.whl (1.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5 MB 15.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake-nltk) (7.1.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake-nltk) (4.62.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake-nltk) (1.1.0)\n",
            "Collecting regex>=2021.8.3\n",
            "  Downloading regex-2021.11.10-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (749 kB)\n",
            "\u001b[K     |████████████████████████████████| 749 kB 62.1 MB/s \n",
            "\u001b[?25hInstalling collected packages: regex, nltk, rake-nltk\n",
            "  Attempting uninstall: regex\n",
            "    Found existing installation: regex 2019.12.20\n",
            "    Uninstalling regex-2019.12.20:\n",
            "      Successfully uninstalled regex-2019.12.20\n",
            "  Attempting uninstall: nltk\n",
            "    Found existing installation: nltk 3.2.5\n",
            "    Uninstalling nltk-3.2.5:\n",
            "      Successfully uninstalled nltk-3.2.5\n",
            "Successfully installed nltk-3.6.7 rake-nltk-1.0.6 regex-2021.11.10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "regex"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qlBd_7LTzy4i",
        "outputId": "9c27ca5b-b3a9-4e55-8954-7ec8d0e60191"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('data mining methods', 9.0),\n",
              " ('operational logic underlying', 9.0),\n",
              " ('data mining models', 9.0),\n",
              " ('modified weighting function', 9.0),\n",
              " ('weighting concepts based', 9.0),\n",
              " ('data mining tasks', 9.0),\n",
              " ('weighted concepts bowc', 8.5),\n",
              " ('low computational costs', 8.5),\n",
              " ('text mining tasks', 8.0),\n",
              " ('represent document vectors', 7.916666666666666)]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from multi_rake import Rake\n",
        "rake = Rake()\n",
        "keywords = rake.apply(full_text)\n",
        "\n",
        "keywords[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Genssim"
      ],
      "metadata": {
        "id": "UCo0cSz00F81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We can use gensim as well for extracting keyword from a given text\n",
        "\n",
        "from gensim.summarization import keywords\n",
        "\n",
        "\n",
        "print(keywords(new_text,words = 10,scores = True, lemmatize = True))"
      ],
      "metadata": {
        "id": "62Zxll48z9lo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "110d90e6-3351-45da-8a10-a97845376f0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('document', 0.295790832867324), ('method', 0.2680239464864187), ('concept', 0.2612895344826125), ('mining', 0.2196749799930402), ('vectors', 0.20536774389917187), ('low', 0.16969327317993818), ('text', 0.16732641830778544), ('benchmark', 0.16098415192646756), ('logic', 0.16098415192646737), ('bag', 0.1566481582487038)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#YAKE"
      ],
      "metadata": {
        "id": "B3cJ3IAH0RwD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install git+https://github.com/LIAAD/yake"
      ],
      "metadata": {
        "id": "NugOYOvT0T3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ccbd13b-30f4-4aef-e23e-2cd4bfaa5bb4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/LIAAD/yake\n",
            "  Cloning https://github.com/LIAAD/yake to /tmp/pip-req-build-zj60j31u\n",
            "  Running command git clone -q https://github.com/LIAAD/yake /tmp/pip-req-build-zj60j31u\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from yake==0.4.8) (0.8.9)\n",
            "Requirement already satisfied: click>=6.0 in /usr/local/lib/python3.7/dist-packages (from yake==0.4.8) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from yake==0.4.8) (1.19.5)\n",
            "Collecting segtok\n",
            "  Downloading segtok-1.5.11-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from yake==0.4.8) (2.6.3)\n",
            "Collecting jellyfish\n",
            "  Downloading jellyfish-0.8.9.tar.gz (137 kB)\n",
            "\u001b[K     |████████████████████████████████| 137 kB 19.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from segtok->yake==0.4.8) (2019.12.20)\n",
            "Building wheels for collected packages: yake, jellyfish\n",
            "  Building wheel for yake (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yake: filename=yake-0.4.8-py2.py3-none-any.whl size=60190 sha256=767720962c75b1f2ab301c3dab808fc45d1388a665785675ab097c8c7c3deb0f\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-t49vx0_e/wheels/52/79/f4/dae9309f60266aa3767a4381405002b6f2955fbcf038d804da\n",
            "  Building wheel for jellyfish (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for jellyfish: filename=jellyfish-0.8.9-cp37-cp37m-linux_x86_64.whl size=73238 sha256=4fd509d961bf3c3c76aa257630d1b1fc6fbe710a114ac9d5441fa2c75c9b1e70\n",
            "  Stored in directory: /root/.cache/pip/wheels/32/a9/ef/5d8742e72deaf0d1de327a180d008c2c0299367581800ea73f\n",
            "Successfully built yake jellyfish\n",
            "Installing collected packages: segtok, jellyfish, yake\n",
            "Successfully installed jellyfish-0.8.9 segtok-1.5.11 yake-0.4.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import yake\n",
        "kw_extractor = yake.KeywordExtractor(top=10, stopwords=None)\n",
        "keywords = kw_extractor.extract_keywords(full_text)\n",
        "\n",
        "for kw, v in keywords:\n",
        "  print(\"Keyphrase: \",kw, \": score\", v)"
      ],
      "metadata": {
        "id": "BrjSFcAd04PG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3705fc2d-0981-41de-c96b-d93e4a369212"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyphrase:  operational logic underlying : score 0.008502958451052589\n",
            "Keyphrase:  text vectorization methods : score 0.015613284939549285\n",
            "Keyphrase:  text vectorization : score 0.02310717508615897\n",
            "Keyphrase:  Traditional text vectorization : score 0.02325791341228692\n",
            "Keyphrase:  data mining models : score 0.02830809004349318\n",
            "Keyphrase:  data mining tasks : score 0.033863083795882626\n",
            "Keyphrase:  DATA MINING : score 0.03618462463953267\n",
            "Keyphrase:  text mining tasks : score 0.037652251074155374\n",
            "Keyphrase:  enables an understanding : score 0.04036782511075581\n",
            "Keyphrase:  operational logic : score 0.04036782511075581\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#textrank"
      ],
      "metadata": {
        "id": "aZ3q6N2P1GDA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install summa"
      ],
      "metadata": {
        "id": "M1BsMskT1IHa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22eff36b-1149-42a3-a3c5-29a4c53e2d0d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting summa\n",
            "  Downloading summa-1.2.0.tar.gz (54 kB)\n",
            "\u001b[?25l\r\u001b[K     |██████                          | 10 kB 27.4 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 20 kB 30.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 30 kB 34.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▉        | 40 kB 28.8 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▉  | 51 kB 22.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 54 kB 2.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=0.19 in /usr/local/lib/python3.7/dist-packages (from summa) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scipy>=0.19->summa) (1.19.5)\n",
            "Building wheels for collected packages: summa\n",
            "  Building wheel for summa (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for summa: filename=summa-1.2.0-py3-none-any.whl size=54410 sha256=b9c92af446509d34d85cd85b8351b3e75e3f35f82703d76ec0bdbb4c8c7cb9c2\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/64/ac/7b443477588d365ef37ada30d456bdf5f07dc5be9f6324cb6e\n",
            "Successfully built summa\n",
            "Installing collected packages: summa\n",
            "Successfully installed summa-1.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from summa import keywords\n",
        "\n",
        "\n",
        "TR_keywords = keywords.keywords(full_text, scores=True)\n",
        "TR_keywords[0:10]"
      ],
      "metadata": {
        "id": "GyGhdD6y1LKe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57c38704-1b83-4c65-fe7e-cbe413f38029"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('methods', 0.29585314188985434),\n",
              " ('method', 0.29585314188985434),\n",
              " ('document', 0.29300649554724484),\n",
              " ('concepts', 0.2597209892723852),\n",
              " ('concept', 0.2597209892723852),\n",
              " ('mining', 0.20425273810869513),\n",
              " ('vectorization', 0.20080655873686565),\n",
              " ('word vectors', 0.18267366210822228),\n",
              " ('computationally', 0.16718186386765732),\n",
              " ('computational', 0.16718186386765732)]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Keybert"
      ],
      "metadata": {
        "id": "gdG4k2FK2ydl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install keybert"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDyRbYJM2x3c",
        "outputId": "6248a787-158b-406e-f38d-2923dfa345e1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting keybert\n",
            "  Downloading keybert-0.5.0.tar.gz (19 kB)\n",
            "Collecting sentence-transformers>=0.3.8\n",
            "  Downloading sentence-transformers-2.1.0.tar.gz (78 kB)\n",
            "\u001b[K     |████████████████████████████████| 78 kB 5.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from keybert) (1.0.1)\n",
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            "Collecting rich>=10.4.0\n",
            "  Downloading rich-10.16.2-py3-none-any.whl (214 kB)\n",
            "\u001b[K     |████████████████████████████████| 214 kB 50.9 MB/s \n",
            "\u001b[?25hCollecting colorama<0.5.0,>=0.4.0\n",
            "  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich>=10.4.0->keybert) (2.6.1)\n",
            "Collecting commonmark<0.10.0,>=0.9.0\n",
            "  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)\n",
            "\u001b[K     |████████████████████████████████| 51 kB 5.7 MB/s \n",
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            "  Attempting uninstall: pyyaml\n",
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            "Successfully installed colorama-0.4.4 commonmark-0.9.1 huggingface-hub-0.2.1 keybert-0.5.0 pyyaml-6.0 rich-10.16.2 sacremoses-0.0.46 sentence-transformers-2.1.0 sentencepiece-0.1.96 tokenizers-0.10.3 transformers-4.15.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install keybert\n",
        "from keybert import KeyBERT"
      ],
      "metadata": {
        "id": "UjFUP5vu20ID"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#You can select any model from sentence-transformers [here](https://www.sbert.net/docs/pretrained_models.html) and pass it through KeyBERT with model:"
      ],
      "metadata": {
        "id": "dAo8XZjf23-E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "NOTE: For a full overview of all possible transformer models see sentence-transformer. I would advise either \"all-MiniLM-L6-v2\" for English documents or \"paraphrase-multilingual-MiniLM-L12-v2\" for multi-lingual documents or any other language.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "T5EbsplV2-LS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kw_model = KeyBERT(model='all-mpnet-base-v2')\n",
        "#all-mpnet-base-v2\n",
        "#all-MiniLM-L6-v2"
      ],
      "metadata": {
        "id": "WIhz2ASv3B9g",
        "colab": {
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        "keywords = kw_model.extract_keywords(full_text, \n",
        "                                     keyphrase_ngram_range=(1, 2), \n",
        "                                     stop_words='english', \n",
        "                                     highlight=True,\n",
        "                                     top_n=10)\n"
      ],
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">VECTORIZATION OF TEXT USING DATA MINING METHODS, In the <span style=\"color: #000000; text-decoration-color: #000000; background-color: #ffff00\">text mining</span> <span style=\"color: #000000; text-decoration-color: #000000; background-color: #ffff00\">textual representation</span> be\n",
              "not only efficient but also interpretable, as this enables an understanding of the \n",
              "operational logic underlying the data mining models. Traditional <span style=\"color: #000000; text-decoration-color: #000000; background-color: #ffff00\">text vectorization</span> such as \n",
              "TF-IDF and bag-of-words are effective and characterized by intuitive interpretability, but \n",
              "suffer from the «curse of dimensionality», and they are unable to capture the meanings of \n",
              "words. On the other hand, modern distributed methods effectively capture the hidden \n",
              "semantics, but they are computationally intensive, time-consuming, and uninterpretable. This \n",
              "article proposes a new <span style=\"color: #000000; text-decoration-color: #000000; background-color: #ffff00\">text vectorization</span> called Bag of weighted Concepts BoWC that presents \n",
              "a document according to the concepts’ information it contains. The proposed method creates \n",
              "concepts by <span style=\"color: #000000; text-decoration-color: #000000; background-color: #ffff00\">clustering word</span> (i.e. word embedding) then uses the frequencies of these <span style=\"color: #000000; text-decoration-color: #000000; background-color: #ffff00\">concept </span>\n",
              "<span style=\"color: #000000; text-decoration-color: #000000; background-color: #ffff00\">clusters</span> represent document vectors. To enrich the resulted document representation, a new \n",
              "modified weighting function is proposed for weighting concepts based on statistics extracted \n",
              "from word embedding information. The generated vectors are characterized by interpretability,\n",
              "low dimensionality, high accuracy, and low computational costs when used in data mining \n",
              "tasks. The proposed method has been tested on five different benchmark datasets in two data \n",
              "mining tasks; <span style=\"color: #000000; text-decoration-color: #000000; background-color: #ffff00\">document clustering</span> classification, and compared with several baselines, \n",
              "including Bag-of-words, TF-IDF, Averaged GloVe, Bag-of-Concepts, and VLAC. The results \n",
              "indicate that BoWC outperforms most baselines and gives 7% better accuracy on average\n",
              "</pre>\n"
            ],
            "text/plain": [
              "VECTORIZATION OF TEXT USING DATA MINING METHODS, In the \u001b[30;48;2;255;255;0mtext mining\u001b[0m \u001b[30;48;2;255;255;0mtextual representation\u001b[0m be\n",
              "not only efficient but also interpretable, as this enables an understanding of the \n",
              "operational logic underlying the data mining models. Traditional \u001b[30;48;2;255;255;0mtext vectorization\u001b[0m such as \n",
              "TF-IDF and bag-of-words are effective and characterized by intuitive interpretability, but \n",
              "suffer from the «curse of dimensionality», and they are unable to capture the meanings of \n",
              "words. On the other hand, modern distributed methods effectively capture the hidden \n",
              "semantics, but they are computationally intensive, time-consuming, and uninterpretable. This \n",
              "article proposes a new \u001b[30;48;2;255;255;0mtext vectorization\u001b[0m called Bag of weighted Concepts BoWC that presents \n",
              "a document according to the concepts’ information it contains. The proposed method creates \n",
              "concepts by \u001b[30;48;2;255;255;0mclustering word\u001b[0m (i.e. word embedding) then uses the frequencies of these \u001b[30;48;2;255;255;0mconcept \u001b[0m\n",
              "\u001b[30;48;2;255;255;0mclusters\u001b[0m represent document vectors. To enrich the resulted document representation, a new \n",
              "modified weighting function is proposed for weighting concepts based on statistics extracted \n",
              "from word embedding information. The generated vectors are characterized by interpretability,\n",
              "low dimensionality, high accuracy, and low computational costs when used in data mining \n",
              "tasks. The proposed method has been tested on five different benchmark datasets in two data \n",
              "mining tasks; \u001b[30;48;2;255;255;0mdocument clustering\u001b[0m classification, and compared with several baselines, \n",
              "including Bag-of-words, TF-IDF, Averaged GloVe, Bag-of-Concepts, and VLAC. The results \n",
              "indicate that BoWC outperforms most baselines and gives 7% better accuracy on average\n"
            ]
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        ""
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}