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KirandeepMaralaKirandeepMarala
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Re-Modifications (#5)
* Add files via upload Have added Exercises and solutions for the language processing Pipeline(Video-9) tutorial. * Add files via upload Have added the exercises and solutions for the Stemming and Lemmatization (Video-10) Tutorial. * Add files via upload have added exercises and solutions for NER (Video-12) Tutorial. * Delete Language_Processing_exercise.ipynb * Delete Language_Processing_exercise_solutions.ipynb * Delete Stemming_and_Lemmatization_Exercise.ipynb * Delete Stemming_and_Lemmatization_Solutions.ipynb * Delete Named_Entity_Recognition_Exercise.ipynb * Delete Named_Entity_Recognition_Solutions.ipynb * Add files via upload * Add files via upload * Add files via upload
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‎5_spacy_lang_processing_pipeline/language_processing_exercise.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Yrci22GYhTQP"
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},
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"source": [
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"### **Spacy Language Processing Pipelines: Exercises**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "YUMPkcohhgam"
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},
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"outputs": [],
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"source": [
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"#importing necessary libraries \n",
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"import spacy\n",
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"\n",
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"nlp = spacy.load(\"en_core_web_sm\") #creating an object and loading the pre-trained model for \"English\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "hxtliEGIh4gS"
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},
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"source": [
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"#### **Excersie: 1**\n",
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"\n",
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"- Get all the proper nouns from a given text in a list and also count how many of them.\n",
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"- **Proper Noun** means a noun that names a particular person, place, or thing."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "lRGfbeEshFf-",
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"outputId": "f8d6beed-c03a-479c-b7bd-4a21173aba55"
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},
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"outputs": [],
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"source": [
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"text = '''Ravi and Raju are the best friends from school days.They wanted to go for a world tour and \n",
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"visit famous cities like Paris, London, Dubai, Rome etc and also they called their another friend Mohan to take part of this world tour.\n",
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"They started their journey from Hyderabad and spent next 3 months travelling all the wonderful cities in the world and cherish a happy moments!\n",
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"'''\n",
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"\n",
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"# https://spacy.io/usage/linguistic-features\n",
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"\n",
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"#creating the nlp object\n",
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"doc = nlp(text) \n",
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"\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "WfU6CRIWhFh8"
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},
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"source": [
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"**Expected Output**\n",
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"\n",
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"Proper Nouns: [Ravi, Raju, Paris, London, Dubai, Rome, Mohan, Hyderabad]\n",
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"\n",
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"Count: 8\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "FUr2rnbYmdlv"
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},
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"source": [
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"#### **Excersie: 2**\n",
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"\n",
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"- Get all companies names from a given text and also the count of them.\n",
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"- **Hint**: Use the spacy **ner** functionality "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "LLf4xyGEmZ2P",
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"outputId": "e9582d9f-4f1e-4574-e3d8-a5526a4fb6cb"
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},
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"outputs": [],
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"source": [
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"text = '''The Top 5 companies in USA are Tesla, Walmart, Amazon, Microsoft, Google and the top 5 companies in \n",
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"India are Infosys, Reliance, HDFC Bank, Hindustan Unilever and Bharti Airtel'''\n",
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"\n",
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"\n",
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"doc = nlp(text)\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "4JK5eMsCmZ5i"
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},
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"source": [
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"**Expected Output**\n",
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"\n",
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"\n",
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"Company Names: [Tesla, Walmart, Amazon, Microsoft, Google, Infosys, Reliance, HDFC Bank, Hindustan Unilever, Bharti Airtel]\n",
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"\n",
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"Count: 10"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "HkbNaNVChFoB"
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},
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"source": [
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"## [**Solution**](./language_processing_exercise_solutions.ipynb)"
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]
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "Language Processing_exercise.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎5_spacy_lang_processing_pipeline/language_processing_exercise_solutions.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Yrci22GYhTQP"
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},
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"source": [
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"### **Spacy Language Processing Pipelines: Solutions**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "YUMPkcohhgam"
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},
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"outputs": [],
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"source": [
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"#importing necessary libraries \n",
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"import spacy\n",
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"\n",
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"nlp = spacy.load(\"en_core_web_sm\") #creating an object and loading the pre-trained model for \"English\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "hxtliEGIh4gS"
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},
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"source": [
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"#### **Excersie: 1**\n",
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"\n",
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"- Get all the proper nouns from a given text in a list and also count how many of them.\n",
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"- **Proper Noun** means a noun that names a particular person, place, or thing."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "lRGfbeEshFf-",
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"outputId": "f8d6beed-c03a-479c-b7bd-4a21173aba55"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Proper Nouns: [Ravi, Raju, Paris, London, Dubai, Rome, Mohan, Hyderabad]\n",
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"Count: 8\n"
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]
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}
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],
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"source": [
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"text = '''Ravi and Raju are the best friends from school days.They wanted to go for a world tour and \n",
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"visit famous cities like Paris, London, Dubai, Rome etc and also they called their another friend Mohan to take part of this world tour.\n",
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"They started their journey from Hyderabad and spent next 3 months travelling all the wonderful cities in the world and cherish a happy moments!\n",
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"'''\n",
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"\n",
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"# https://spacy.io/usage/linguistic-features\n",
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"\n",
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"#creating the nlp object\n",
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"doc = nlp(text) \n",
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"\n",
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"\n",
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"#list for storing the proper nouns\n",
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"all_proper_nouns = [] \n",
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"\n",
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"\n",
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"for token in doc:\n",
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" if token.pos_ == \"PROPN\": #checking the whether token belongs to parts of speech \"PROPN\" [Proper Noun]\n",
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" all_proper_nouns.append(token)\n",
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" \n",
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"\n",
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"#finally printing the results\n",
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"print(\"Proper Nouns: \", all_proper_nouns)\n",
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"print(\"Count: \", len(all_proper_nouns))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "FUr2rnbYmdlv"
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},
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"source": [
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"#### **Excersie: 2**\n",
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"\n",
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"- Get all companies names from a given text and also the count of them.\n",
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"- **Hint**: Use the spacy **ner** functionality "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "LLf4xyGEmZ2P",
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"outputId": "e9582d9f-4f1e-4574-e3d8-a5526a4fb6cb"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Company Names: [Tesla, Walmart, Amazon, Microsoft, Google, Infosys, Reliance, HDFC Bank, Hindustan Unilever, Bharti Airtel]\n",
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"Count: 10\n"
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]
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}
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],
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"source": [
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"text = '''The Top 5 companies in USA are Tesla, Walmart, Amazon, Microsoft, Google and the top 5 companies in \n",
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"India are Infosys, Reliance, HDFC Bank, Hindustan Unilever and Bharti Airtel'''\n",
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"\n",
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"\n",
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"doc = nlp(text)\n",
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"\n",
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"#list for storing the company names\n",
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"all_company_names = []\n",
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"\n",
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"for ent in doc.ents:\n",
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" if ent.label_ == 'ORG': #checking the whether token belongs to entity \"ORG\" [Organisation]\n",
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" all_company_names.append(ent)\n",
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"\n",
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"\n",
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"\n",
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"#finally printing the results\n",
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"print(\"Company Names: \", all_company_names)\n",
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"print(\"Count: \", len(all_company_names))"
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]
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "Language Processing_exercise.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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