adding 3 nltk notebooks
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9810e70fd3
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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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"source": [
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"# NLTK - Frequency Distribution"
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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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"source": [
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"https://www.nltk.org/book/ch01.html#frequency-distributions"
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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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"outputs": [],
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"source": [
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"import nltk\n",
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"import random"
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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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"outputs": [],
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"source": [
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"lines = open('txt/language.txt').readlines()\n",
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"sentence = random.choice(lines)\n",
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"print(sentence)"
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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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"source": [
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"## Tokens"
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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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"outputs": [],
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"source": [
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"tokens = nltk.word_tokenize(sentence)\n",
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"print(tokens)"
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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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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Frequency Distribution"
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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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"outputs": [],
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"source": [
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"# frequency of characters\n",
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"fd = nltk.FreqDist(sentence)\n",
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"print(fd)"
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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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"outputs": [],
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"source": [
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"print(fd.most_common(50))"
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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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"outputs": [],
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"source": []
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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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"outputs": [],
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"source": [
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"# frequency of words\n",
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"fd = nltk.FreqDist(tokens)\n",
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"print(fd)"
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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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"outputs": [],
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"source": [
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"print(fd.most_common(50))"
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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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"outputs": [],
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"source": []
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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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"outputs": [],
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"source": [
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"# frequency of a text\n",
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"txt = open('txt/language.txt').read()\n",
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"tokens = nltk.word_tokenize(txt)\n",
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"fd = nltk.FreqDist(tokens)\n",
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"print(fd)"
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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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"outputs": [],
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"source": [
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"print(fd.most_common(50))"
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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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"outputs": [],
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"source": []
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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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"outputs": [],
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"source": [
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"# Requesting the frequency of a specific word\n",
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"print(fd['language'])"
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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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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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@ -0,0 +1,350 @@
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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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"source": [
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"# NLTK - Part of Speech"
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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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"outputs": [],
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"source": [
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"import nltk\n",
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"import random"
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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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"outputs": [],
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"source": [
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"lines = open('txt/language.txt').readlines()\n",
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"sentence = random.choice(lines)\n",
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"print(sentence)"
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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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"source": [
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"## Tokens"
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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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"outputs": [],
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"source": [
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"tokens = nltk.word_tokenize(sentence)\n",
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"print(tokens)"
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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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"source": [
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"## Part of Speech \"tags\""
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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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"outputs": [],
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"source": [
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"tagged = nltk.pos_tag(tokens)\n",
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"print(tagged)"
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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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"source": [
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"Now, you could select for example all the type of **verbs**:"
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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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"outputs": [],
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"source": [
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"selection = []\n",
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"\n",
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"for word, tag in tagged:\n",
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" if 'VB' in tag:\n",
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" selection.append(word)\n",
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"\n",
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"print(selection)"
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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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"source": [
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"### Where do these tags come from?"
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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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"source": [
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"> An off-the-shelf tagger is available for English. It uses the Penn Treebank tagset.\n",
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"\n",
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"From: http://www.nltk.org/api/nltk.tag.html#module-nltk.tag"
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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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"source": [
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"> NLTK provides documentation for each tag, which can be queried using the tag, e.g. nltk.help.upenn_tagset('RB').\n",
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"\n",
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"From: http://www.nltk.org/book_1ed/ch05.html"
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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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"outputs": [],
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"source": [
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"nltk.help.upenn_tagset('PRP')"
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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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"source": [
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"------------"
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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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"source": [
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"An alphabetical list of part-of-speech tags used in the Penn Treebank Project ([link](https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html)):\n",
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"\n",
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"<table cellspacing=\"2\" cellpadding=\"2\" border=\"0\">\n",
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" <tbody><tr bgcolor=\"#DFDFFF\" align=\"none\"> \n",
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" <td align=\"none\"> \n",
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" <div align=\"left\">Number</div>\n",
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" </td>\n",
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" <td> \n",
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" <div align=\"left\">Tag</div>\n",
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" </td>\n",
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" <td> \n",
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" <div align=\"left\">Description</div>\n",
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" </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 1. </td>\n",
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" <td>CC </td>\n",
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" <td>Coordinating conjunction </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 2. </td>\n",
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" <td>CD </td>\n",
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" <td>Cardinal number </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 3. </td>\n",
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" <td>DT </td>\n",
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" <td>Determiner </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 4. </td>\n",
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" <td>EX </td>\n",
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" <td>Existential <i>there<i> </i></i></td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 5. </td>\n",
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" <td>FW </td>\n",
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" <td>Foreign word </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 6. </td>\n",
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" <td>IN </td>\n",
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" <td>Preposition or subordinating conjunction </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 7. </td>\n",
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" <td>JJ </td>\n",
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" <td>Adjective </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 8. </td>\n",
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" <td>JJR </td>\n",
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" <td>Adjective, comparative </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 9. </td>\n",
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" <td>JJS </td>\n",
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" <td>Adjective, superlative </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 10. </td>\n",
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" <td>LS </td>\n",
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" <td>List item marker </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 11. </td>\n",
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" <td>MD </td>\n",
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" <td>Modal </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 12. </td>\n",
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" <td>NN </td>\n",
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" <td>Noun, singular or mass </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 13. </td>\n",
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" <td>NNS </td>\n",
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" <td>Noun, plural </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 14. </td>\n",
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" <td>NNP </td>\n",
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" <td>Proper noun, singular </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 15. </td>\n",
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" <td>NNPS </td>\n",
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" <td>Proper noun, plural </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 16. </td>\n",
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" <td>PDT </td>\n",
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" <td>Predeterminer </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 17. </td>\n",
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" <td>POS </td>\n",
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" <td>Possessive ending </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 18. </td>\n",
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" <td>PRP </td>\n",
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" <td>Personal pronoun </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 19. </td>\n",
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" <td>PRP\\$ </td>\n",
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" <td>Possessive pronoun </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 20. </td>\n",
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" <td>RB </td>\n",
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" <td>Adverb </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 21. </td>\n",
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" <td>RBR </td>\n",
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" <td>Adverb, comparative </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 22. </td>\n",
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" <td>RBS </td>\n",
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" <td>Adverb, superlative </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 23. </td>\n",
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" <td>RP </td>\n",
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" <td>Particle </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 24. </td>\n",
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" <td>SYM </td>\n",
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" <td>Symbol </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 25. </td>\n",
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" <td>TO </td>\n",
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" <td><i>to</i> </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 26. </td>\n",
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" <td>UH </td>\n",
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" <td>Interjection </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 27. </td>\n",
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" <td>VB </td>\n",
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" <td>Verb, base form </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 28. </td>\n",
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" <td>VBD </td>\n",
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" <td>Verb, past tense </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 29. </td>\n",
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" <td>VBG </td>\n",
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" <td>Verb, gerund or present participle </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 30. </td>\n",
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" <td>VBN </td>\n",
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" <td>Verb, past participle </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 31. </td>\n",
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" <td>VBP </td>\n",
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" <td>Verb, non-3rd person singular present </td>\n",
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" </tr>\n",
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" <tr bgcolor=\"#FFFFCA\"> \n",
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" <td align=\"none\"> 32. </td>\n",
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" <td>VBZ </td>\n",
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" <td>Verb, 3rd person singular present </td>\n",
|
||||
" </tr>\n",
|
||||
" <tr bgcolor=\"#FFFFCA\"> \n",
|
||||
" <td align=\"none\"> 33. </td>\n",
|
||||
" <td>WDT </td>\n",
|
||||
" <td>Wh-determiner </td>\n",
|
||||
" </tr>\n",
|
||||
" <tr bgcolor=\"#FFFFCA\"> \n",
|
||||
" <td align=\"none\"> 34. </td>\n",
|
||||
" <td>WP </td>\n",
|
||||
" <td>Wh-pronoun </td>\n",
|
||||
" </tr>\n",
|
||||
" <tr bgcolor=\"#FFFFCA\"> \n",
|
||||
" <td align=\"none\"> 35. </td>\n",
|
||||
" <td>WP$ </td>\n",
|
||||
" <td>Possessive wh-pronoun </td>\n",
|
||||
" </tr>\n",
|
||||
" <tr bgcolor=\"#FFFFCA\"> \n",
|
||||
" <td align=\"none\"> 36. </td>\n",
|
||||
" <td>WRB </td>\n",
|
||||
" <td>Wh-adverb \n",
|
||||
"</td></tr></tbody></table>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@ -0,0 +1,165 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# NLTK - Similar Words"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"https://www.nltk.org/book/ch01.html#searching-text"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import nltk"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"txt = open('txt/language.txt').read()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Tokens"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tokens = nltk.word_tokenize(txt)\n",
|
||||
"print(tokens)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## NLTK Text object"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"text = nltk.Text(tokens)\n",
|
||||
"print(text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## concordance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# This is what you did with Michael before the break ...\n",
|
||||
"concordance = text.concordance(\"language\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## similarities"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# With a small next step ...\n",
|
||||
"similar = text.similar(\"language\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# And searching for contexts ...\n",
|
||||
"contexts = text.common_contexts([\"language\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"----------------"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Read on"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"https://www.nltk.org/book/ch01.html#searching-text (recommended!)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue