@ -6,21 +6,28 @@ Add a sketchnote if possible/appropriate
## [Pre-lecture quiz](link-to-quiz-app)
For most *Natural Language Processing* tasks, the text to be processed must be broken down, examined, and the results stored or cross referenced with rules and data sets. This allows the programmer to derive the meaning or intent or even just the frequency of terms and words in a text. Here are a list of common techniques used in processing text. You should know these are because they are combined with machine learning techniques to analyse large amounts of text efficiently. In the next lesson, you'll learn how to code some of these.
For most *Natural Language Processing* tasks, the text to be processed must be broken down, examined, and the results stored or cross referenced with rules and data sets. This allows the programmer to derive the meaning or intent or even just the frequency of terms and words in a text.
### Tokenization
Let's discover common techniques used in processing text. Combined with machine learning, these techniques help you to analyse large amounts of text efficiently.
## The Tools of NLP
Probably the first thing most NLP algorithms have to do is split the text into tokens, or words. While this sounds simple, having to take punctuation and different language's word and sentence delimiters can make it tricky.
> 🎓 **Tokenization**
>
> Probably the first thing most NLP algorithms have to do is split the text into tokens, or words. While this sounds simple, having to take punctuation and different language's word and sentence delimiters can make it tricky.
### Parsing & Part-of-speech Tagging
> 🎓 **Parsing & Part-of-speech Tagging**
>
> Every word that has been tokenised can be tagged as a part of speech - is the word a noun, a verb, or adjective etc. The sentence `the quick red fox jumped over the lazy brown dog` might be POS tagged as *fox* = noun, *jumped* = verb etc.
>
> Parsing is recognising what words are related to each other in a sentence - for instance `the quick red fox jumped` is an adjective-noun-verb sequence that is is separate from `lazy brown dog` sequence.
Every word that has been tokenised can be tagged as a part of speech - is the word a noun, a verb, or adjective etc. The sentence `the quick red fox jumped over the lazy brown dog` might be POS tagged as *fox* = noun, *jumped* = verb etc.
> 🎓 **Word and Phrase Frequencies**
>
> A useful tool when analysing a large body of text is to build a dictionary of every word or phrase of interest and how often it appears. The phrase `the quick red fox jumped over the lazy brown dog` has a word frequency of 2 for `the`.
Parsing is recognising what words are related to each other in a sentence - for instance `the quick red fox jumped` is an adjective-noun-verb sequence that is is separate from `lazy brown dog` sequence.
### Example:
### Word and phrase Frequencies
A useful tool when analysing a large body of text is to build a dictionary of every word or phrase of interest and how often it appears. The phrase `the quick red fox jumped over the lazy brown dog` has a word frequency of 2 for `the`. The Rudyard Kipling poem *The Winners* has a verse:
The Rudyard Kipling poem *The Winners* has a verse:
```
What the moral? Who rides may read.
@ -31,11 +38,13 @@ Down to Gehenna or up to the Throne,
He travels the fastest who travels alone.
```
As phrases frequencies can be case insensitive or case sensitive as required, the phrase `a friend` has a frequency of 2 and `the` has a frequency of 6, and `travels` is 2.
As phrase frequencies can be case insensitive or case sensitive as required, the phrase `a friend` has a frequency of 2 and `the` has a frequency of 6, and `travels` is 2.
### n-grams
> 🎓 **N-grams**
>
> A text can be split into sequences of words of a set length, a single word (unigram), two words (bigrams), three words (trigrams) or any number of words (n-grams).
A text can be split into sequences of words of a set length, a single word (unigram), two words (bigrams), three words (trigrams) or any number of words (n-grams).
### Example
For instance `the quick red fox jumped over the lazy brown dog` with a n-gram score of 2 produces the following n-grams:
@ -60,27 +69,34 @@ It might be easier to visualise it as a sliding box over the sentence. Here it i
7. the quick red fox jumped over <u>**the lazy brown**</u> dog
8. the quick red fox jumped over the **<u>lazy brown dog</u>**
### Noun phrase Extraction
In most sentences, there is a noun that is the subject, or object of the sentence. In English, it is often identifiable as it has 'a' or 'an' or the preceding it. Identifying the subject or object of a sentence by 'extracting the noun phrase' is a common task in NLP when attempting to understand the meaning of a sentence.
> 🎓 **Noun phrase Extraction**
>
> In most sentences, there is a noun that is the subject, or object of the sentence. In English, it is often identifiable as having 'a' or 'an' or 'the' preceding it. Identifying the subject or object of a sentence by 'extracting the noun phrase' is a common task in NLP when attempting to understand the meaning of a sentence.
### Example
In the sentence `the quick red fox jumped over the lazy brown dog` there are 2 noun phrases: **quick red fox** and **lazy brown dog**.
### Sentiment analysis
A sentence or text can be analysed for sentiment, or how *positive* or *negative* it is. Sentiment is measured in *polarity* and *objectivity/subjectivity*. Polarity is measured from -1.0 to 1.0 (negative to positive) and 0.0 to 1.0 (most objective to most subjective). Later this lesson you'll learn there are different ways to determine sentiment using machine learning, but one way is to have a list of words and phrases that are categorised as positive or negative by a human expert and apply that model to text to calculate a polarity score.
> 🎓 **Sentiment analysis**
>
> A sentence or text can be analysed for sentiment, or how *positive* or *negative* it is. Sentiment is measured in *polarity* and *objectivity/subjectivity*. Polarity is measured from -1.0 to 1.0 (negative to positive) and 0.0 to 1.0 (most objective to most subjective).
### WordNet, Inflection and lemmatization
✅ Later you'll learn that there are different ways to determine sentiment using machine learning, but one way is to have a list of words and phrases that are categorised as positive or negative by a human expert and apply that model to text to calculate a polarity score. Can you see how this would work in some circumstances and less well in others?
[WordNet](https://wordnet.princeton.edu/) is a database of words, synonyms, antonyms and many other details for every word in many different languages. It is incredibly useful when attempting to build translations, spell checkers, or language tools of any type.
> 🎓 **WordNet**
>
> [WordNet](https://wordnet.princeton.edu/) is a database of words, synonyms, antonyms and many other details for every word in many different languages. It is incredibly useful when attempting to build translations, spell checkers, or language tools of any type.
Inflection enables you to take a word and get the singular, or plural of the word.
> 🎓 **Inflection**
>
> Inflection enables you to take a word and get the singular or plural of the word.
A *lemma* is the root or headword for a set of words, for instance *flew*, *flies*, *flying* have a lemma of the verb *fly*.
> 🎓 **Lemmatization**
>
> A *lemma* is the root or headword for a set of words, for instance *flew*, *flies*, *flying* have a lemma of the verb *fly*.
### TextBlob & NLTK
## TextBlob & NLTK
Luckily, you don't have to build all of these techniques yourself, as there are excellent Python libraries available that make it much more accessible to developers who haven't specialised in natural language processing or machine learning. The next lesson includes more examples on these, but here you will learn some useful examples to help you with the next task.
Luckily, you don't have to build all of these techniques yourself, as there are excellent Python libraries available that make it much more accessible to developers who aren't specialised in natural language processing or machine learning. The next lesson includes more examples on these, but here you will learn some useful examples to help you with the next task.
> Note: A useful [Quick Start](https://textblob.readthedocs.io/en/dev/quickstart.html#quickstart) guide is available for TextBlob that is recommended for experienced Python developers
Next, you'll make Marvin a bit more sympathetic by analysing our input for sentiment, and printing out a response to match the sentiment. You'll also need to identify a noun_phrase and ask about it.
In the previous lesson you built a very simple Q&A bot. Now, you'll make Marvin a bit more sympathetic by analyzing your input for sentiment and printing out a response to match the sentiment. You'll also need to identify a `noun_phrase` and ask about it.
Your steps when building a better conversational bot:
@ -113,7 +128,7 @@ Your steps when building a better conversational bot:
5. Print response
3. loop back to step 2
Here is the code snippet to determine sentiment using TextBlob, note I only have 4*gradients* of sentiment response (you could have more if you like):
Here is the code snippet to determine sentiment using TextBlob. Note there are only four*gradients* of sentiment response (you could have more if you like):
```python
if user_input_blob.polarity <= -0.5:
@ -144,112 +159,20 @@ Hmm, that's not great. Can you tell me more about old hounddogs?
> bye
It was nice talking to you, goodbye!
```
One possible solution to the task is [here](solutions/lesson1_task2.py)
One possible solution to the task is [here](solution/bot.py)
✅ Knowledge Check
1. Do you think the sympathetic responses would 'trick' someone into thinking that the bot actually understood them?
2. Does identifying the noun phrase make the bot more 'believable'?
3. Why would extracting a 'noun phrase' from a sentence a useful thing to do?
### Translation
An important challenge in computational linguistics is accurate *translation* of a sentence from one spoken or written language to another. This is a very hard problem compounded by the fact that there are thousands of languages and each can have very different grammar rules. One approach is to convert the formal grammar rules for one language, such as English, into a non-language dependent structure, and then translate it by converting back to another language. This means that you would take the following steps:
1. Identify or tag the words in input language into nouns, verbs etc.
2. Produce a direct translation of each word in the target language format
Example: In **English**, the simple sentence `I feel happy` is 3 words in the order **subject** (I), **verb** (feel), **adjective** (happy). However, in the **Irish** language, the same sentence has a very different grammatical structure - emotions like "*happy*" or "*sad*" are expressed as being *upon* you. The English phrase `I feel happy` in Irish would be `Tá athas orm`. A *literal* translation would be `Happy is upon me`. Of course, an Irish speaker translating to English would say `I feel happy`, not `Happy is upon me`, because they understand the meaning of the sentence, even if the words and sentence structure are different. The formal order for the sentence in Irish are **verb** (Tá or is), **adjective** (athas, or happy), **subject** (orm, or upon me).
A simple (and poor) translation program might translate words only, ignoring the sentence structure. This leads to bad (and sometimes hilarious) mistranslations: `I feel happy` translates literally to `Mise bhraitheann athas`. In Irish that means (literally) `me feel happy` and not a valid Irish sentence. Even though English and Irish are languages spoken on two adjoining islands, they are very different languages with different grammar structures.
### Machine Learning Approaches
So far, you've learned about the formal rules approach to natural language processing. Another approach is to ignore the meaning of the words, and instead use machine learning to detect patterns. This can work in translation if you have lots of text (a *corpus*) or texts (*corpora*) in both the origin and target languages. For instance, if you have *Pride and Prejudice* in English and a human translation of the book in *French*, you could detect phrases in one are idiomatically translated into the other.
For instance, when an English phrase such as `John looked at the cake with a wolfish grin` is translated literally, to, say French, it might become `John regarda le gâteau avec un sourire de loup`. A reader of both languages would understand that the direct translation of `wolfish grin` is not the French translation `wolf smile` but a synonym - in this case for being very hungry or voracious. A better translation that a human might make would be `John regarda le gâteau avec voracité`, because it better conveys the meaning. If a ML model has enough human translations to build a model on, it can improve the accuracy of translations by identifying common patterns in texts that have been previously translated by expert human speakers of both languages.
Another area where machine learning can work very well is sentiment analysis. A non-ML approach to sentiment is to identify words and phrases which are 'positive' and 'negative'. Then, given a new piece of text, calculate the total value of the positive, negative and neutral words to identify the overall sentiment. This approach is easily tricked as you may have seen in the Marvin task - the sentence `Great, that was a wonderful waste of time, I'm glad we are lost on this dark road` is a sarcastic, negative sentiment sentence, but the simple algorithm detects 'great', 'wonderful', 'glad' as positive and 'waste', 'lost' and 'dark' as negative. The overall sentiment is swayed by these conflicting words.
The ML approach would be to hand gather negative and positive bodies of text - tweets, or movie reviews, or anything where the human has given a score *and* a written opinion. Then NLP techniques can be applied to opinions and scores, so that patterns emerge (e.g., positive movie reviews tend to have the phrase 'Oscar worthy' more than negative movie reviews, or positive restaurant reviews say 'gourmet' much more than 'disgusting').
**Example**: If you worked in a politician's office and there was some new law being debated, constituents might write to the office with emails supporting or emails against the particular new law. Let's say you are tasked with reading the emails and sorting them in 2 piles, *for* and *against*. If there were a lot of emails, you might be overwhelmed attempting to read them all. Wouldn't it be nice if a bot could read them all for you, understand them and tell you which pile each email belonged? One way to achieve that is to use Machine Learning. You would train the model with a portion of the *against* emails and a portion of the *for* emails. The model would tend to associate phrases and words with the against side and the for side, *but it would not understand any of the content*, only that certain words and patterns were more likely to appear in an *against* or a *for* email. You could test it with some emails that you had not used to train the model, and see if it came to the same conclusion as you did. Then, once you were happy with the accuracy of the model, you could process future emails without having to read each one.
### Task: Sentimental Sentences
Sentiment is measured in with a *polarity* of -1 to 1, meaning -1 is the most negative sentiment, and 1 is the most positive. Sentiment is also measured with an 0 - 1 score for objectivity (0) and subjectivity (1).
**Pride and Prejudice**, by **Jane Austen** is an English novel with some famous lines. The text is available here at [Project Gutenberg](https://www.gutenberg.org/files/1342/1342-h/1342-h.htm). The sample below shows a short program which analyses the sentiment of first and last sentences from the book and display its sentiment polarity and subjectivity/objectivity score. You should us the TextBlob library (described above) to determine sentiment (you do not have to write your own sentiment calculator) in the following task.
```python
from textblob import TextBlob
quote1 = """It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife."""
quote2 = """Darcy, as well as Elizabeth, really loved them; and they were both ever sensible of the warmest gratitude towards the persons who, by bringing her into Derbyshire, had been the means of uniting them."""
sentiment1 = TextBlob(quote1).sentiment
sentiment2 = TextBlob(quote2).sentiment
print(quote1 + " has a sentiment of " + str(sentiment1))
print(quote2 + " has a sentiment of " + str(sentiment2))
# outputs:
# It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want # of a wife. has a sentiment of Sentiment(polarity=0.20952380952380953, subjectivity=0.27142857142857146)
# Darcy, as well as Elizabeth, really loved them; and they were
# both ever sensible of the warmest gratitude towards the persons
# who, by bringing her into Derbyshire, had been the means of
# uniting them. has a sentiment of Sentiment(polarity=0.7, subjectivity=0.8)
```
Your task is to determine, using sentiment polarity, if *Pride and Prejudice* has more absolutely positive sentences than absolutely negative ones. For this task, you may assume that a polarity score of 1 or -1 is absolutely positive or negative respectively.
Steps:
1. Download a copy of Pride and Prejudice from Project Gutenberg as a .txt file. Remove the metadata at the start and end of the file, leaving only the original text
2. Open the file in Python and extract the contents as a string
3. Create a TextBlob using the book string
4. Analyse each sentence in the book in a loop
1. If the polarity is 1 or -1 store the sentence in an array or list of positive or negative messages
5. At the end, print out all the positive sentences and negative sentences (separately) and the number of each.
Here is a sample [solution](solutions/lesson1_task3.py).
✅ Knowledge Check
1. The sentiment is based on words used in the sentence, but does it code *understand* the words?
2. Do you think the sentiment polarity is accurate, or in other words, do you *agree* with the scores?
1. In particular, do you agree or disagree with the absolute **positive** polarity of the following sentences?
* “What an excellent father you have, girls!” said she, when the door was shut.
* “Your examination of Mr. Darcy is over, I presume,” said Miss Bingley; “and pray what is the result?” “I am perfectly convinced by it that Mr. Darcy has no defect.
* How wonderfully these sort of things occur!
* I have the greatest dislike in the world to that sort of thing.
* Charlotte is an excellent manager, I dare say.
* “This is delightful indeed!
* I am so happy!
* Your idea of the ponies is delightful.
2. The next 3 sentences were scored with an absolute positive sentiment, but on close reading, they are not positive sentences. Why did the sentiment analysis think they were positive sentences?
* Happy shall I be, when his stay at Netherfield is over!” “I wish I could say anything to comfort you,” replied Elizabeth; “but it is wholly out of my power.
* If I could but see you as happy!
* Our distress, my dear Lizzy, is very great.
3. Do you agree or disagree with the absolute **negative** polarity of the following sentences?
- Everybody is disgusted with his pride.
- “I should like to know how he behaves among strangers.” “You shall hear then—but prepare yourself for something very dreadful.
- The pause was to Elizabeth’s feelings dreadful.
- It would be dreadful!
🚀 Challenge: Can you make Marvin even better by extracting other features from the user input?
## 🚀Challenge
Add a challenge for students to work on collaboratively in class to enhance the project
Optional: add a screenshot of the completed lesson's UI if appropriate
An important challenge in computational linguistics is accurate *translation* of a sentence from one spoken or written language to another. This is a very hard problem compounded by the fact that there are thousands of languages and each can have very different grammar rules. One approach is to convert the formal grammar rules for one language, such as English, into a non-language dependent structure, and then translate it by converting back to another language. This means that you would take the following steps:
1. Identify or tag the words in input language into nouns, verbs etc.
2. Produce a direct translation of each word in the target language format
Example: In **English**, the simple sentence `I feel happy` is 3 words in the order **subject** (I), **verb** (feel), **adjective** (happy). However, in the **Irish** language, the same sentence has a very different grammatical structure - emotions like "*happy*" or "*sad*" are expressed as being *upon* you. The English phrase `I feel happy` in Irish would be `Tá athas orm`. A *literal* translation would be `Happy is upon me`. Of course, an Irish speaker translating to English would say `I feel happy`, not `Happy is upon me`, because they understand the meaning of the sentence, even if the words and sentence structure are different. The formal order for the sentence in Irish are **verb** (Tá or is), **adjective** (athas, or happy), **subject** (orm, or upon me).
A simple (and poor) translation program might translate words only, ignoring the sentence structure. This leads to bad (and sometimes hilarious) mistranslations: `I feel happy` translates literally to `Mise bhraitheann athas`. In Irish that means (literally) `me feel happy` and not a valid Irish sentence. Even though English and Irish are languages spoken on two adjoining islands, they are very different languages with different grammar structures.
### Machine Learning Approaches
So far, you've learned about the formal rules approach to natural language processing. Another approach is to ignore the meaning of the words, and instead use machine learning to detect patterns. This can work in translation if you have lots of text (a *corpus*) or texts (*corpora*) in both the origin and target languages. For instance, if you have *Pride and Prejudice* in English and a human translation of the book in *French*, you could detect phrases in one are idiomatically translated into the other.
For instance, when an English phrase such as `John looked at the cake with a wolfish grin` is translated literally, to, say French, it might become `John regarda le gâteau avec un sourire de loup`. A reader of both languages would understand that the direct translation of `wolfish grin` is not the French translation `wolf smile` but a synonym - in this case for being very hungry or voracious. A better translation that a human might make would be `John regarda le gâteau avec voracité`, because it better conveys the meaning. If a ML model has enough human translations to build a model on, it can improve the accuracy of translations by identifying common patterns in texts that have been previously translated by expert human speakers of both languages.
Another area where machine learning can work very well is sentiment analysis. A non-ML approach to sentiment is to identify words and phrases which are 'positive' and 'negative'. Then, given a new piece of text, calculate the total value of the positive, negative and neutral words to identify the overall sentiment. This approach is easily tricked as you may have seen in the Marvin task - the sentence `Great, that was a wonderful waste of time, I'm glad we are lost on this dark road` is a sarcastic, negative sentiment sentence, but the simple algorithm detects 'great', 'wonderful', 'glad' as positive and 'waste', 'lost' and 'dark' as negative. The overall sentiment is swayed by these conflicting words.
The ML approach would be to hand gather negative and positive bodies of text - tweets, or movie reviews, or anything where the human has given a score *and* a written opinion. Then NLP techniques can be applied to opinions and scores, so that patterns emerge (e.g., positive movie reviews tend to have the phrase 'Oscar worthy' more than negative movie reviews, or positive restaurant reviews say 'gourmet' much more than 'disgusting').
**Example**: If you worked in a politician's office and there was some new law being debated, constituents might write to the office with emails supporting or emails against the particular new law. Let's say you are tasked with reading the emails and sorting them in 2 piles, *for* and *against*. If there were a lot of emails, you might be overwhelmed attempting to read them all. Wouldn't it be nice if a bot could read them all for you, understand them and tell you which pile each email belonged? One way to achieve that is to use Machine Learning. You would train the model with a portion of the *against* emails and a portion of the *for* emails. The model would tend to associate phrases and words with the against side and the for side, *but it would not understand any of the content*, only that certain words and patterns were more likely to appear in an *against* or a *for* email. You could test it with some emails that you had not used to train the model, and see if it came to the same conclusion as you did. Then, once you were happy with the accuracy of the model, you could process future emails without having to read each one.
### Task: Sentimental Sentences
Sentiment is measured in with a *polarity* of -1 to 1, meaning -1 is the most negative sentiment, and 1 is the most positive. Sentiment is also measured with an 0 - 1 score for objectivity (0) and subjectivity (1).
**Pride and Prejudice**, by **Jane Austen** is an English novel with some famous lines. The text is available here at [Project Gutenberg](https://www.gutenberg.org/files/1342/1342-h/1342-h.htm). The sample below shows a short program which analyses the sentiment of first and last sentences from the book and display its sentiment polarity and subjectivity/objectivity score. You should us the TextBlob library (described above) to determine sentiment (you do not have to write your own sentiment calculator) in the following task.
```python
from textblob import TextBlob
quote1 = """It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife."""
quote2 = """Darcy, as well as Elizabeth, really loved them; and they were both ever sensible of the warmest gratitude towards the persons who, by bringing her into Derbyshire, had been the means of uniting them."""
sentiment1 = TextBlob(quote1).sentiment
sentiment2 = TextBlob(quote2).sentiment
print(quote1 + " has a sentiment of " + str(sentiment1))
print(quote2 + " has a sentiment of " + str(sentiment2))
# outputs:
# It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want # of a wife. has a sentiment of Sentiment(polarity=0.20952380952380953, subjectivity=0.27142857142857146)
# Darcy, as well as Elizabeth, really loved them; and they were
# both ever sensible of the warmest gratitude towards the persons
# who, by bringing her into Derbyshire, had been the means of
# uniting them. has a sentiment of Sentiment(polarity=0.7, subjectivity=0.8)
```
Your task is to determine, using sentiment polarity, if *Pride and Prejudice* has more absolutely positive sentences than absolutely negative ones. For this task, you may assume that a polarity score of 1 or -1 is absolutely positive or negative respectively.
Steps:
1. Download a copy of Pride and Prejudice from Project Gutenberg as a .txt file. Remove the metadata at the start and end of the file, leaving only the original text
2. Open the file in Python and extract the contents as a string
3. Create a TextBlob using the book string
4. Analyse each sentence in the book in a loop
1. If the polarity is 1 or -1 store the sentence in an array or list of positive or negative messages
5. At the end, print out all the positive sentences and negative sentences (separately) and the number of each.
Here is a sample [solution](solutions/lesson1_task3.py).
✅ Knowledge Check
1. The sentiment is based on words used in the sentence, but does it code *understand* the words?
2. Do you think the sentiment polarity is accurate, or in other words, do you *agree* with the scores?
1. In particular, do you agree or disagree with the absolute **positive** polarity of the following sentences?
* “What an excellent father you have, girls!” said she, when the door was shut.
* “Your examination of Mr. Darcy is over, I presume,” said Miss Bingley; “and pray what is the result?” “I am perfectly convinced by it that Mr. Darcy has no defect.
* How wonderfully these sort of things occur!
* I have the greatest dislike in the world to that sort of thing.
* Charlotte is an excellent manager, I dare say.
* “This is delightful indeed!
* I am so happy!
* Your idea of the ponies is delightful.
2. The next 3 sentences were scored with an absolute positive sentiment, but on close reading, they are not positive sentences. Why did the sentiment analysis think they were positive sentences?
* Happy shall I be, when his stay at Netherfield is over!” “I wish I could say anything to comfort you,” replied Elizabeth; “but it is wholly out of my power.
* If I could but see you as happy!
* Our distress, my dear Lizzy, is very great.
3. Do you agree or disagree with the absolute **negative** polarity of the following sentences?
- Everybody is disgusted with his pride.
- “I should like to know how he behaves among strangers.” “You shall hear then—but prepare yourself for something very dreadful.
- The pause was to Elizabeth’s feelings dreadful.
- It would be dreadful!
🚀 Challenge: Can you make Marvin even better by extracting other features from the user input?
Jen Looper
4 years ago