diff --git "a/week20_nlp_hw_ipynb\354\235\230_\354\202\254\353\263\270.ipynb" "b/week20_nlp_hw_ipynb\354\235\230_\354\202\254\353\263\270.ipynb"
new file mode 100644
index 0000000..fef14bf
--- /dev/null
+++ "b/week20_nlp_hw_ipynb\354\235\230_\354\202\254\353\263\270.ipynb"
@@ -0,0 +1,447 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "week20_nlp_hw.ipynb의 사본",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "📌 week20 과제는 **18주차의 Constituency Parsing TreeRNNS 실습**으로 구성되어 있습니다.\n",
+        "\n",
+        "📌 위키독스의 딥러닝을 이용한 자연어 처리 입문 교재 실습, 관련 블로그 등의 문서 자료로 구성되어 있는 과제입니다. \n",
+        "\n",
+        "📌 안내된 링크에 맞추어 **직접 코드를 따라 치면서 (필사)** 해당 nlp task 의 기본적인 라이브러리와 메서드를 숙지해보시면 좋을 것 같습니다😊 필수라고 체크한 부분은 과제에 반드시 포함시켜주시고, 선택으로 체크한 부분은 자율적으로 스터디 하시면 됩니다.\n",
+        "\n",
+        "📌 궁금한 사항은 깃허브 이슈나, 카톡방, 세션 발표 시작 이전 시간 등을 활용하여 자유롭게 공유해주세요!"
+      ],
+      "metadata": {
+        "id": "BX3ac8Ag1RPC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🥰 **이하 예제를 실습하시면 됩니다.**\n",
+        "\n",
+        "**1-2는 필수과제입니다.**\n",
+        "\n",
+        "실습보다는 개념 이해가 주인 단원이라, 실습 과제는 적습니다. TreeRNN은 현실적으로 잘 쓰이지 않기 때문이죠. 왜 잘 쓰이지 않을까요? 이 질문에 대해 생각해 보는 것으로 이번 과제를 시작해 봅시다."
+      ],
+      "metadata": {
+        "id": "Kq8aMYKGPQR0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "`your answer here`"
+      ],
+      "metadata": {
+        "id": "ZJLIfgQ9vlNe"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **1️⃣ Probabilistic Parsing 실습**"
+      ],
+      "metadata": {
+        "id": "SHTPAk95iNtP"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "📌 [Probabilistic Context Free Grammars](https://lost-contact.mit.edu/afs/cs.pitt.edu/projects/nltk/docs/tutorial/pcfg/nochunks.html#pcfg) \n",
+        "\n",
+        "TreeRNN에 PCFG rule을 적용해 만든 것이 Syntactically-United RNN이죠. PCFG rule을 실습해 봅시다."
+      ],
+      "metadata": {
+        "id": "9L-jAHPkiBV0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sentence = \"\"\"At eight o'clock on Thursday morning\n",
+        "... Arthur didn't feel very good.\"\"\"\n",
+        "tokens = nltk.word_tokenize(sentence)\n",
+        "tokens"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XOGPFvyJWoW0",
+        "outputId": "13a312ce-897d-47ef-b897-ec9c97ba7278"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['At',\n",
+              " 'eight',\n",
+              " \"o'clock\",\n",
+              " 'on',\n",
+              " 'Thursday',\n",
+              " 'morning',\n",
+              " 'Arthur',\n",
+              " 'did',\n",
+              " \"n't\",\n",
+              " 'feel',\n",
+              " 'very',\n",
+              " 'good',\n",
+              " '.']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tagged = nltk.pos_tag(tokens)\n",
+        "tagged[0:6]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4SmAH4lpU9cS",
+        "outputId": "2eb44e67-3619-4aa4-96e7-d4d12eac5c5b"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[('At', 'IN'),\n",
+              " ('eight', 'CD'),\n",
+              " (\"o'clock\", 'NN'),\n",
+              " ('on', 'IN'),\n",
+              " ('Thursday', 'NNP'),\n",
+              " ('morning', 'NN')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "entities = nltk.chunk.ne_chunk(tagged)\n",
+        "entities"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "ZiOVpEpjeDso",
+        "outputId": "01430795-0c2a-45a6-9b1e-428c842c11b9"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ModuleNotFoundError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 338\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    339\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nltk/tree/tree.py\u001b[0m in \u001b[0;36m_repr_svg_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_repr_svg_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m         \u001b[0;32mfrom\u001b[0m \u001b[0msvgling\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdraw_tree\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    784\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdraw_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_repr_svg_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'svgling'"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Tree('S', [('At', 'IN'), ('eight', 'CD'), (\"o'clock\", 'NN'), ('on', 'IN'), ('Thursday', 'NNP'), ('morning', 'NN'), Tree('PERSON', [('Arthur', 'NNP')]), ('did', 'VBD'), (\"n't\", 'RB'), ('feel', 'VB'), ('very', 'RB'), ('good', 'JJ'), ('.', '.')])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **2️⃣ Tree Based Algorithm**"
+      ],
+      "metadata": {
+        "id": "HfTr_BPwGc8D"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "📌 [Decision Tree](https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/ml-decision-tree/tutorial/) 실습\n",
+        "\n",
+        "참고: [Embedding Graphs with Deep Learning 읽기 자료](https://towardsdatascience.com/embedding-graphs-with-deep-learning-55e0c66d7752)\n",
+        "\n",
+        "Decision Tree는 머신 러닝에서 자주 쓰이는 Tree Algorith입니다. Decision Tree를 실습해 보며 Tree 구조와 알고리즘을 이해해 봅시다.\n"
+      ],
+      "metadata": {
+        "id": "0HnXNyCAwSHJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Importing required libraries\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.datasets import load_iris\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.model_selection import train_test_split"
+      ],
+      "metadata": {
+        "id": "svtikbdZatBY"
+      },
+      "execution_count": 50,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Loading the iris data\n",
+        "data = load_iris()\n",
+        "print('Classes to predict: ', data.target_names)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "--tIW4ikeyC9",
+        "outputId": "498d53cd-b5c0-4628-9254-7af085ed5032"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classes to predict:  ['setosa' 'versicolor' 'virginica']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Extracting data attributes\n",
+        "X = data.data\n",
+        "### Extracting target/ class labels\n",
+        "y = data.target\n",
+        "\n",
+        "print('Number of examples in the data:', X.shape[0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "J4epi16eezp4",
+        "outputId": "4130b657-f9bf-4150-b42e-91d3026cd973"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of examples in the data: 150\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#First four rows in the variable 'X'\n",
+        "X[:4]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oH5lMSude1Jp",
+        "outputId": "9d5806c8-efd1-493e-c238-5ade5f202cf2"
+      },
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[5.1, 3.5, 1.4, 0.2],\n",
+              "       [4.9, 3. , 1.4, 0.2],\n",
+              "       [4.7, 3.2, 1.3, 0.2],\n",
+              "       [4.6, 3.1, 1.5, 0.2]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Using the train_test_split to create train and test sets.\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 47, test_size = 0.25)"
+      ],
+      "metadata": {
+        "id": "wAM_o-4Ie3QV"
+      },
+      "execution_count": 57,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Importing the Decision tree classifier from the sklearn library.\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "clf = DecisionTreeClassifier(criterion = 'entropy')"
+      ],
+      "metadata": {
+        "id": "FQRJM_dFfoGG"
+      },
+      "execution_count": 64,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Training the decision tree classifier. \n",
+        "clf.fit(X_train, y_train)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KrSFO54afprP",
+        "outputId": "0f3d0f9d-b8d5-4de5-a85c-a3ad585168df"
+      },
+      "execution_count": 65,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DecisionTreeClassifier(criterion='entropy')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 65
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Predicting labels on the test set.\n",
+        "y_pred =  clf.predict(X_test)"
+      ],
+      "metadata": {
+        "id": "DylDmUIWfrW9"
+      },
+      "execution_count": 66,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Importing the accuracy metric from sklearn.metrics library\n",
+        "\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "print('Accuracy Score on train data: ', accuracy_score(y_true=y_train, y_pred=clf.predict(X_train)))\n",
+        "print('Accuracy Score on test data: ', accuracy_score(y_true=y_test, y_pred=y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xyVcWoougS4l",
+        "outputId": "fc811c1c-e66c-4bbf-fee3-9f1818750aef"
+      },
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy Score on train data:  1.0\n",
+            "Accuracy Score on test data:  0.9736842105263158\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "clf = DecisionTreeClassifier(criterion='entropy', min_samples_split=50)\n",
+        "clf.fit(X_train, y_train)\n",
+        "print('Accuracy Score on train data: ', accuracy_score(y_true=y_train, y_pred=clf.predict(X_train)))\n",
+        "print('Accuracy Score on the test data: ', accuracy_score(y_true=y_test, y_pred=clf.predict(X_test)))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-FFc4F2CgUt5",
+        "outputId": "f4ec459b-dbc4-405a-f4fc-3163b34df2a5"
+      },
+      "execution_count": 68,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy Score on train data:  0.9553571428571429\n",
+            "Accuracy Score on the test data:  0.9736842105263158\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 참고 자료\n",
+        "\n",
+        "- [RNTN](https://ratsgo.github.io/deep%20learning/2017/06/24/RNTN/)\n",
+        "\n",
+        "- [When and Why Tree-Based Models (Often) Outperform Neural Networks](https://towardsdatascience.com/when-and-why-tree-based-models-often-outperform-neural-networks-ceba9ecd0fd8)\n",
+        "\n",
+        "- 저번 주차 내용과 관련된 페이퍼 [Parsing with Compositional Vector Grammars](https://nlp.stanford.edu/pubs/SocherBauerManningNg_ACL2013.pdf) 도 도움이 될 거예요! :)\n",
+        "\n",
+        "- [RNN과 LSTM이 왜 잘 안 쓰이는지에 대한 칼럼](https://medium.com/towards-data-science/the-fall-of-rnn-lstm-2d1594c74ce0)도 한번 읽어 보시면 좋을 것 같습니다."
+      ],
+      "metadata": {
+        "id": "pNDTxHF3sxo8"
+      }
+    }
+  ]
+}
\ No newline at end of file