# Andrew's NLP Adventures in Wonderland¶

Natural Language Processing (NLP) is a fascinating field: the first gateway towards human-friendly user experience. In sci-fi movies decades before personal and mobile computing, we witnessed how the public vision of how we interact with intelligent machines in ways we would interact with fellow human beings. This vision is made possible today with the advances in NLP through machine learning, deep learning, and the wide adoption of conversational interface via chatbots (Messenger, Telegram, etc.) and voice assistants (Alexa, Google Assistant).

Today, we will explore a fundamental concept in NLP: Language Model. A Language Model is a probability distribution over sequences of words that affords the ability to predict the next word in the sequence. In this brief, we will build a language model via two approaches: first from scratch using a word-pair frequency approach, then with an n-gram approach to further expand the model utilizing an NLP library. Along the exploratory path, we will look at sentiments exhibited across the chapters, and uncover the relationships between the characters. We will end the tour with some pointers to expand this analysis and apply the lessons to real-world problems.

## The Problem¶

1. Create a method that calculates the frequency of word pairs in a text file.
2. Create a method that returns the word that is the most likely successor of the word that is given as a parameter.

## Data Cleaning¶

Before jumping onto the task of language modeling, it is imperative to inspect the data and clean it as necessary.

In [1]:
import requests

# read the text directly from the internet
response = requests.get("https://www.gutenberg.org/files/11/11-0.txt")
rawtxt = response.text

# inspect the first and last 500 characters
print(f"footer>>\n{rawtxt[-500:]}")

header>>
﻿Project Gutenberg’s Alice’s Adventures in Wonderland, by Lewis Carroll

This eBook is for the use of anyone anywhere at no cost and with
almost no restrictions whatsoever.  You may copy it, give it away or
re-use it under the terms of the Project Gutenberg License included
with this eBook or online at www.gutenberg.org

Author: Lewis Carroll

Posting Date: June 25, 2008 [EBook #11]
Release Date: March, 1994
Last Updated: October 6, 2016

footer>>
ss a copyright notice is included.  Thus, we do not necessarily
keep eBooks in compliance with any particular paper edition.

Most people start at our Web site which has the main PG search facility:

http://www.gutenberg.org

This Web site includes information about Project Gutenberg-tm,
including how to make donations to the Project Gutenberg Literary
Archive Foundation, how to help produce our new eBooks, and how to



## Preprocessing¶

The unicode text file is littered with eol(end-of-line) characters that should be cleaned in preprocessing. Let's look at some summary statistics of the text. Upon inspection the Project Gutenberg header and footer should be removed.

In [2]:
int_txt = rawtxt.split("*** START OF THIS PROJECT GUTENBERG EBOOK ALICE’S ADVENTURES IN WONDERLAND ***")[1].split("*** END OF THIS PROJECT GUTENBERG EBOOK ALICE’S ADVENTURES IN WONDERLAND ***")[0]
print(f"footer>>{int_txt[-50:]}")

header>>

footer>>e’s Adventures in Wonderland, by Lewis Carroll



# Language Modeling¶

## Language Model by frequency of word pairs¶

Here's our first attempt at the language modeling problem in predicting the next word using frequency. First, tally all consecutive word pairs and their frequency across the whole book. Then write a function to look up a word, and give its most likely successor by frequency. The task is first accomplished from scratch without using any NLP library.

### From scratch¶

In [3]:
from collections import Counter
txt_vec = int_txt.lower().split()
txt_pairs = zip(txt_vec[:-1], txt_vec[1:])
cnt = Counter(txt_pairs)
def getMostLikelySuccessor(word):
return sorted([(cnt[x], x) for x in cnt if x[0].startswith(word.lower())], reverse=True)[0][1][1]

[getMostLikelySuccessor(x) for x in ["Alice", "was", "at"]] == ["was", "a", "the"]

Out[3]:
True

### Using NLP libraries¶

Next, we perform the same task then further explore the text using the textacy library, which affords convenient text preprocessing methods.

In [4]:
import textacy
doc = textacy.Doc(textacy.preprocess_text(int_txt))
bot2 = doc.to_bag_of_terms(ngrams=2, as_strings=True, normalize="lower", named_entities=False, filter_stops=False)

In [5]:
def getNextWord(word, bot, n):
try:
return sorted([(y, x) for x, y in bot.items() if x.startswith(word.lower())], reverse=True)[0][1].split(" ")[n-1]
except IndexError:
return "<EOL>"

[getNextWord(x, bot2, 2) for  x in ["Alice", "was", "at"]] == ["was", "a", "the"]

Out[5]:
True

### Remarks¶

What we just built was a bigram language model based on frequency. However, bigrams are not very good in preserving word coherence in the sequence.

In [6]:
def textGen(seed, bot, m, n):
'''seed: the first word, bot: bag of terms, m: number of words in the sequence'''
result = seed.split(" ")
seed = " ".join(result[-n+1:])
for i in range(m):
if "<EOL>" not in result:
new_word = getNextWord(seed, bot, n)
result.append(new_word)
if n > 2:
seed = " ".join(result[-n+1:])
else:
seed = new_word
return " ".join(result)

textGen("Alice", bot2, 10, 2)

Out[6]:
'Alice was a the queen <EOL>'

Let's modify the current language model to include larger n-grams, say trigrams (3) and 4-gram and compare the results.

In [7]:
[f'{n}-gram: {textGen("Alice was going to", doc.to_bag_of_terms(ngrams=n, as_strings=True, normalize="lower", named_entities=False, filter_stops=False), 20, n)}' for n in range(2, 5)]


Out[7]:
['2-gram: Alice was going to the queen <EOL>',
'3-gram: Alice was going to begin with,’ the mock turtle said with some surprise that the was <EOL>',
'4-gram: Alice was going to shrink any further <EOL>']

In the case of the 4-gram, it has become so restrictive (due to a large n and a relatively short story) that an original line from the book was returned.

# Exploratory Data Analysis¶

## Word frequency by chapter¶

It is often helpful to perform summary statistics to have an idea of the "shape" of the data. It is especially important to spot imbalanced classes due to data availability to avoid biases. Here we inspect the various basic word counts in each chapter of the text. The chapters are well balanced except for a slightly shorter chapter 3 (A Caucus-Race and a Long Tale). Chapter 6 (Pig and Pepper) demonstrates a relative lack of word diversity per sentence. Curiously enough, that is the chapter where the quote "How do you know I'm mad" lives.

In [8]:
import pandas as pd
chapters = textacy.preprocess_text(int_txt).split("CHAPTER")[1:]
corp = textacy.Corpus("en", chapters)
ts = [textacy.TextStats(x) for x in corp]
basic_counts = pd.DataFrame([x.basic_counts for x in ts])
basic_counts["chapter"] = range(1, 13)
basic_counts["unique_word_ratio"] = basic_counts['n_unique_words'] / basic_counts['n_sents']
basic_counts

Out[8]:
n_chars n_long_words n_monosyllable_words n_polysyllable_words n_sents n_syllables n_unique_words n_words chapter unique_word_ratio
0 8722 233 1765 63 106 2697 632 2191 1 5.962264
1 8388 218 1840 56 132 2597 633 2186 2 4.795455
2 7070 225 1396 71 119 2166 598 1739 3 5.025210
3 10709 294 2215 67 167 3318 737 2727 4 4.413174
4 9130 303 1824 101 136 2774 664 2240 5 4.882353
5 10660 333 2188 89 149 3265 726 2669 6 4.872483
6 9781 285 1910 73 172 2942 678 2381 7 3.941860
7 10638 352 2053 116 122 3211 688 2556 8 5.639344
8 9777 328 1906 77 145 2902 712 2355 9 4.910345
9 8802 315 1727 89 152 2568 627 2097 10 4.125000
10 8029 254 1589 69 121 2378 575 1942 11 4.752066
11 9046 268 1785 91 127 2718 696 2198 12 5.480315

## Sentiments across the chapters¶

Sentiment analysis concerns quantifying the polarity of emotion expressed in a text ranging from negative (-1), neutral (0), to positive (1). Subjectivity measures whether the text is objective (0) or subjective (1) with a varying degree. We will briefly explore the sentiments across the chapters using the TextBlob library, which provides a simplified interface for sentiment analysis at various levels of the text (chapter, sentence, etc.).

In [9]:
from textblob import TextBlob
tb = [TextBlob(chapter) for chapter in chapters]
sentiment = pd.DataFrame([chapter.sentiment for chapter in tb])
sentiment["chapter"] = range(1, 13)

Out[9]:
polarity subjectivity chapter
0 0.027015 0.502594 1
1 0.026922 0.511728 2
2 0.037310 0.458711 3
3 0.036413 0.501831 4
4 0.006358 0.469411 5
In [10]:
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
polarity = [sentence.sentiment.polarity for chapter in tb for sentence in chapter.sentences]
plt.scatter(x=list(range(len(polarity))), y=polarity);

In [11]:
# relationship between subjectivity and polarity
subjectivity = [sentence.sentiment.subjectivity for chapter in tb for sentence in chapter.sentences]
plt.scatter(x=polarity, y=subjectivity);


We observe a slight positive trend in the sentiment polarity as the plot progresses. And that there's a positive correspondence between subjectivity and the magnitude in polarity.

## Characters appearance across chapters¶

It would be of interest to correlate characters and the associated sentiment variation in the plot. A first step would be to count the character appearances per chapter. The list of characters is extracted from this site.

In [12]:
from collections import defaultdict
chars = ['alice', 'rabbit', 'caterpillar', 'cat', 'cheshire', 'queen', 'sister', 'dinah', 'mouse', 'duck', 'dodo', 'lory', 'eaglet', 'crab', 'mary', 'pat','bill', 'guinea', 'puppy', 'pigeon', 'frog-footman', 'fish-footman', 'duchess', 'baby', 'cook', 'march', 'dormouse', 'elsie', 'lacie', 'tillie', 'five', 'seven', 'two', 'knave', 'king', 'flamingos', 'grphon', 'turtle', 'juror']
char_counts = defaultdict(list)
for chapter in tb:
for char in chars:
char_counts[char].append(chapter.words.lemmatize().count(char))
char_counts = pd.DataFrame(char_counts)
char_counts["chapter"] = range(1, 13)
# WIP: to collect and plot major character apperances across chapters

Out[12]:
alice baby bill cat caterpillar cheshire cook crab dinah dodo ... pigeon puppy queen rabbit seven sister tillie turtle two chapter
0 28 0 0 5 0 0 0 0 5 0 ... 0 0 0 6 0 2 0 0 2 1
1 26 0 0 8 0 0 0 0 1 1 ... 0 0 0 4 1 0 0 0 1 2
2 23 0 0 2 0 0 0 2 5 12 ... 0 0 0 0 0 0 0 0 1 3
3 31 0 14 0 1 0 0 0 3 0 ... 0 7 0 15 0 0 0 0 4 4
4 35 0 0 0 26 0 0 0 0 0 ... 12 0 0 0 0 0 0 0 3 5

5 rows × 40 columns

## Social Network in the Wonderland¶

It would be very informative to visualize the characters' social network in the wonderland. Our first approach identifies and counts the cooccurrences of characters in nearby words, then computes some centrality metrics concerning the nodes that show their relative importance. Betweeness quantifies the number of times a node acts as a bridge along the shortest path between two other nodes. The number of cliques (or complete sub-graphs) indicates how many tightly related community a node (character) is in. The visualized graph is created using Cytoscape, where the size of the node indicates the node betweeness, the size of the node label (name) indicates the number of cliques the character is in, and the thickness of the edge illustrates counts of cooccurrences.

In [13]:
import networkx as nx
g = doc.to_semantic_network()
g.remove_edges_from(g.selfloop_edges())

In [14]:
from networkx.algorithms import community
sub_g = g.subgraph(chars)

for node, attr in nx.betweenness_centrality(sub_g).items():
sub_g.nodes[node]['betweeness'] = attr

for node, attr in nx.number_of_cliques(sub_g).items():
sub_g.nodes[node]['n_cliques'] = attr

# nx.write_graphml(sub_g, "data/sub_g_clique.graphml")


# Next Steps¶

Thank you for taking this quick tour with me in the wonderland. There is a lot more to explore, for example:

• Sophisticated language models beyond word frequency (such as those based on recurrent neural networks (RNN))
• Accurate character identification in text using part of speech (POS)
• Subtle insights into character relationship via sentiment analysis of their dialogues

Feel free to read this if you want to dive deeper into the source material and its literary analyses.