Natural Language Processing

ACTL3143 & ACTL5111 Deep Learning for Actuaries

Patrick Laub

Natural Language Processing

Lecture Outline

• Natural Language Processing

• Car Crash Police Reports

• Text Vectorisation

• Bag Of Words

• Limiting The Vocabulary

• Intelligently Limit The Vocabulary

• Word Embeddings

• Word Embeddings II

• Car Crash NLP Part II

What is NLP?

A field of research at the intersection of computer science, linguistics, and artificial intelligence that takes the naturally spoken or written language of humans and processes it with machines to automate or help in certain tasks

How the computer sees text

Spot the odd one out:

[112, 97, 116, 114, 105, 99, 107, 32, 108, 97, 117, 98]

[80, 65, 84, 82, 73, 67, 75, 32, 76, 65, 85, 66]

[76, 101, 118, 105, 32, 65, 99, 107, 101, 114, 109, 97, 110]

Generated by:

print([ord(x) for x in "patrick laub"])
print([ord(x) for x in "PATRICK LAUB"])
print([ord(x) for x in "Levi Ackerman"])

The ord built-in turns characters into their ASCII form.

Question

The largest value for a character is 127, can you guess why?

ASCII

American Standard Code for Information Interchange

Unicode is the new standard.

Random strings

The built-in chr function turns numbers into characters.

rnd.seed(1)

chars = [chr(rnd.randint(32, 127)) for _ in range(10)]
chars

['E', ',', 'h', ')', 'k', '%', 'o', '`', '0', '!']

" ".join(chars)

'E , h ) k % o ` 0 !'

"".join([chr(rnd.randint(32, 127)) for _ in range(50)])

"lg&9R42t+<=.Rdww~v-)'_]6Y! \\q(x-Oh>g#f5QY#d8Kl:TpI"

"".join([chr(rnd.randint(0, 128)) for _ in range(50)])

'R\x0f@D\x19obW\x07\x1a\x19h\x16\tCg~\x17}d\x1b%9S&\x08 "\n\x17\x0foW\x19Gs\\J>. X\x177AqM\x03\x00x'

Escape characters

print("Hello,\tworld!")

Hello,  world!

print("Line 1\nLine 2")

Line 1
Line 2

print("Patrick\rLaub")

Laubick

print("C:\tom\new folder")

C:  om
ew folder

Escape the backslash:

print("C:\\tom\\new folder")

C:\tom\new folder

repr("Hello,\rworld!")

"'Hello,\\rworld!'"

Non-natural language processing I

How would you evaluate

10 + 2 * -3

All that Python sees is a string of characters.

[ord(c) for c in "10 + 2 * -3"]

[49, 48, 32, 43, 32, 50, 32, 42, 32, 45, 51]

10 + 2 * -3

4

Non-natural language processing II

Python first tokenizes the string:

import tokenize
import io

code = "10 + 2 * -3"
tokens = tokenize.tokenize(io.BytesIO(code.encode("utf-8")).readline)
for token in tokens:
    print(token)

TokenInfo(type=63 (ENCODING), string='utf-8', start=(0, 0), end=(0, 0), line='')
TokenInfo(type=2 (NUMBER), string='10', start=(1, 0), end=(1, 2), line='10 + 2 * -3')
TokenInfo(type=54 (OP), string='+', start=(1, 3), end=(1, 4), line='10 + 2 * -3')
TokenInfo(type=2 (NUMBER), string='2', start=(1, 5), end=(1, 6), line='10 + 2 * -3')
TokenInfo(type=54 (OP), string='*', start=(1, 7), end=(1, 8), line='10 + 2 * -3')
TokenInfo(type=54 (OP), string='-', start=(1, 9), end=(1, 10), line='10 + 2 * -3')
TokenInfo(type=2 (NUMBER), string='3', start=(1, 10), end=(1, 11), line='10 + 2 * -3')
TokenInfo(type=4 (NEWLINE), string='', start=(1, 11), end=(1, 12), line='')
TokenInfo(type=0 (ENDMARKER), string='', start=(2, 0), end=(2, 0), line='')

Non-natural language processing III

Python needs to parse the tokens into an abstract syntax tree.

import ast

print(ast.dump(ast.parse("10 + 2 * -3"), indent="  "))

Module(
  body=[
    Expr(
      value=BinOp(
        left=Constant(value=10),
        op=Add(),
        right=BinOp(
          left=Constant(value=2),
          op=Mult(),
          right=UnaryOp(
            op=USub(),
            operand=Constant(value=3)))))],
  type_ignores=[])

graph TD;
    Expr --> C[Add]
    C --> D[10]
    C --> E[Mult]
    E --> F[2]
    E --> G[USub]
    G --> H[3]

Non-natural language processing IV

The abstract syntax tree is then compiled into bytecode.

import dis

def expression(a, b, c):
    return a + b * -c

dis.dis(expression)

  3           0 RESUME                   0

  4           2 LOAD_FAST                0 (a)
              4 LOAD_FAST                1 (b)
              6 LOAD_FAST                2 (c)
              8 UNARY_NEGATIVE
             10 BINARY_OP                5 (*)
             14 BINARY_OP                0 (+)
             18 RETURN_VALUE

Running the bytecode

ChatGPT tokenization

https://platform.openai.com/tokenizer

Example of GPT 3.5/4’s tokenization

Applications of NLP in Industry

1) Classifying documents: Using the language within a body of text to classify it into a particular category, e.g.:

• Grouping emails into high and low urgency
• Movie reviews into positive and negative sentiment (i.e. sentiment analysis)
• Company news into bullish (positive) and bearish (negative) statements

2) Machine translation: Assisting language translators with machine-generated suggestions from a source language (e.g. English) to a target language

Applications of NLP in Industry

3) Search engine functions, including:

• Autocomplete
• Predicting what information or website user is seeking

4) Speech recognition: Interpreting voice commands to provide information or take action. Used in virtual assistants such as Alexa, Siri, and Cortana

Deep learning & NLP?

Simple NLP applications such as spell checkers and synonym suggesters do not require deep learning and can be solved with deterministic, rules-based code with a dictionary/thesaurus.

More complex NLP applications such as classifying documents, search engine word prediction, and chatbots are complex enough to be solved using deep learning methods.

NLP in 1966-1973 #1

A typical story occurred in early machine translation efforts, which were generously funded by the U.S. National Research Council in an attempt to speed up the translation of Russian scientific papers in the wake of the Sputnik launch in 1957. It was thought initially that simple syntactic transformations, based on the grammars of Russian and English, and word replacement from an electronic dictionary, would suffice to preserve the exact meanings of sentences.

NLP in 1966-1973 #2

The fact is that accurate translation requires background knowledge in order to resolve ambiguity and establish the content of the sentence. The famous retranslation of “the spirit is willing but the flesh is weak” as “the vodka is good but the meat is rotten” illustrates the difficulties encountered. In 1966, a report by an advisory committee found that “there has been no machine translation of general scientific text, and none is in immediate prospect.” All U.S. government funding for academic translation projects was canceled.

High-level history of deep learning

A brief history of deep learning.

Car Crash Police Reports

Lecture Outline

• Natural Language Processing

• Car Crash Police Reports

• Text Vectorisation

• Bag Of Words

• Limiting The Vocabulary

• Intelligently Limit The Vocabulary

• Word Embeddings

• Word Embeddings II

• Car Crash NLP Part II

Downloading the dataset

Look at the (U.S.) National Highway Traffic Safety Administration’s (NHTSA) National Motor Vehicle Crash Causation Survey (NMVCCS) dataset.

from pathlib import Path

if not Path("NHTSA_NMVCCS_extract.parquet.gzip").exists():
    print("Downloading dataset")                                    
    !wget https://github.com/JSchelldorfer/ActuarialDataScience/raw/master/12%20-%20NLP%20Using%20Transformers/NHTSA_NMVCCS_extract.parquet.gzip

df = pd.read_parquet("NHTSA_NMVCCS_extract.parquet.gzip")
print(f"shape of DataFrame: {df.shape}")

shape of DataFrame: (6949, 16)

Features

• level_0, index, SCASEID: all useless row numbers
• SUMMARY_EN and SUMMARY_GE: summaries of the accident
• NUMTOTV: total number of vehicles involved in the accident
• WEATHER1 to WEATHER8 (not one-hot):
  • WEATHER1: cloudy
  • WEATHER2: snow
  • WEATHER3: fog, smog, smoke
  • WEATHER4: rain
  • WEATHER5: sleet, hail (freezing drizzle or rain)
  • WEATHER6: blowing snow
  • WEATHER7: severe crosswinds
  • WEATHER8: other
• INJSEVA and INJSEVB: injury severity & (binary) presence of bodily injury

Crash summaries

df["SUMMARY_EN"]

0       V1, a 2000 Pontiac Montana minivan, made a lef...
1       The crash occurred in the eastbound lane of a ...
2       This crash occurred just after the noon time h...
                              ...                        
6946    The crash occurred in the eastbound lanes of a...
6947    This single-vehicle crash occurred in a rural ...
6948    This two vehicle daytime collision occurred mi...
Name: SUMMARY_EN, Length: 6949, dtype: object

df["SUMMARY_EN"].map(lambda summary: len(summary)).hist(grid=False);

A crash summary

df["SUMMARY_EN"].iloc[1]

"The crash occurred in the eastbound lane of a two-lane, two-way asphalt roadway on level grade.  The conditions were daylight and wet with cloudy skies in the early afternoon on a weekday.\t\r \r V1, a 1995 Chevrolet Lumina was traveling eastbound.  V2, a 2004 Chevrolet Trailblazer was also traveling eastbound on the same roadway.  V2, was attempting to make a left-hand turn into a private drive on the North side of the roadway.  While turning V1 attempted to pass V2 on the left-hand side contacting it's front to the left side of V2.  Both vehicles came to final rest on the roadway at impact.\r \r The driver of V1 fled the scene and was not identified, so no further information could be obtained from him.  The Driver of V2 stated that the driver was a male and had hit his head and was bleeding.  She did not pursue the driver because she thought she saw a gun. The officer said that the car had been reported stolen.\r \r The Critical Precrash Event for the driver of V1 was this vehicle traveling over left lane line on the left side of travel.  The Critical Reason for the Critical Event was coded as unknown reason for the critical event because the driver was not available. \r \r The driver of V2 was a 41-year old female who had reported that she had stopped prior to turning to make sure she was at the right house.  She was going to show a house for a client.  She had no health related problems.  She had taken amoxicillin.  She does not wear corrective lenses and felt rested.  She was not injured in the crash.\r \r The Critical Precrash Event for the driver of V2 was other vehicle encroachment from adjacent lane over left lane line.  The Critical Reason for the Critical Event was not coded for this vehicle and the driver of V2 was not thought to have contributed to the crash."

Carriage returns

print(df["SUMMARY_EN"].iloc[1])

The Critical Precrash Event for the driver of V2 was other vehicle encroachment from adjacent lane over left lane line.  The Critical Reason for the Critical Event was not coded for this vehicle and the driver of V2 was not thought to have contributed to the crash.r corrective lenses and felt rested.  She was not injured in the crash. of V2.  Both vehicles came to final rest on the roadway at impact.

# Replace every \r with \n
def replace_carriage_return(summary):
    return summary.replace("\r", "\n")

df["SUMMARY_EN"] = df["SUMMARY_EN"].map(replace_carriage_return)
print(df["SUMMARY_EN"].iloc[1][:500])

The crash occurred in the eastbound lane of a two-lane, two-way asphalt roadway on level grade.  The conditions were daylight and wet with cloudy skies in the early afternoon on a weekday.    
 
 V1, a 1995 Chevrolet Lumina was traveling eastbound.  V2, a 2004 Chevrolet Trailblazer was also traveling eastbound on the same roadway.  V2, was attempting to make a left-hand turn into a private drive on the North side of the roadway.  While turning V1 attempted to pass V2 on the left-hand side contactin

Target

Predict number of vehicles in the crash.

df["NUMTOTV"].value_counts()\
    .sort_index()

NUMTOTV
1    1822
2    4151
3     783
4     150
5      34
6       5
7       2
8       1
9       1
Name: count, dtype: int64

np.sum(df["NUMTOTV"] > 3)

193

Simplify the target to just:

• 1 vehicle
• 2 vehicles
• 3+ vehicles

df["NUM_VEHICLES"] = \
  df["NUMTOTV"].map(lambda x: \
    str(x) if x <= 2 else "3+")
df["NUM_VEHICLES"].value_counts()\
  .sort_index()

NUM_VEHICLES
1     1822
2     4151
3+     976
Name: count, dtype: int64

Just ignore this for now…

rnd.seed(123)

for i, summary in enumerate(df["SUMMARY_EN"]):
    word_numbers = ["one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten"]
    num_cars = 10
    new_car_nums = [f"V{rnd.randint(100, 10000)}" for _ in range(num_cars)]
    num_spaces = 4

    for car in range(1, num_cars+1):
        new_num = new_car_nums[car-1]
        summary = summary.replace(f"V-{car}", new_num)
        summary = summary.replace(f"Vehicle {word_numbers[car-1]}", new_num).replace(f"vehicle {word_numbers[car-1]}", new_num)
        summary = summary.replace(f"Vehicle #{word_numbers[car-1]}", new_num).replace(f"vehicle #{word_numbers[car-1]}", new_num)
        summary = summary.replace(f"Vehicle {car}", new_num).replace(f"vehicle {car}", new_num)
        summary = summary.replace(f"Vehicle #{car}", new_num).replace(f"vehicle #{car}", new_num)
        summary = summary.replace(f"Vehicle # {car}", new_num).replace(f"vehicle # {car}", new_num)

        for j in range(num_spaces+1):
            summary = summary.replace(f"V{' '*j}{car}", new_num).replace(f"V{' '*j}#{car}", new_num).replace(f"V{' '*j}# {car}", new_num)
            summary = summary.replace(f"v{' '*j}{car}", new_num).replace(f"v{' '*j}#{car}", new_num).replace(f"v{' '*j}# {car}", new_num)
         
    df.loc[i, "SUMMARY_EN"] = summary

Convert y to integers & split the data

from sklearn.preprocessing import LabelEncoder
target_labels = df["NUM_VEHICLES"]
target = LabelEncoder().fit_transform(target_labels)
target

array([1, 1, 1, ..., 2, 0, 1])

weather_cols = [f"WEATHER{i}" for i in range(1, 9)]
features = df[["SUMMARY_EN"] + weather_cols]

X_main, X_test, y_main, y_test = \
    train_test_split(features, target, test_size=0.2, random_state=1)

# As 0.25 x 0.8 = 0.2
X_train, X_val, y_train, y_val = \
    train_test_split(X_main, y_main, test_size=0.25, random_state=1)

X_train.shape, X_val.shape, X_test.shape

((4169, 9), (1390, 9), (1390, 9))

print([np.mean(y_train == y) for y in [0, 1, 2]])

[0.25833533221396016, 0.6032621731830176, 0.1384024946030223]

Text Vectorisation

Lecture Outline

• Natural Language Processing

• Car Crash Police Reports

• Text Vectorisation

• Bag Of Words

• Limiting The Vocabulary

• Intelligently Limit The Vocabulary

• Word Embeddings

• Word Embeddings II

• Car Crash NLP Part II

Grab the start of a few summaries

first_summaries = X_train["SUMMARY_EN"].iloc[:3]
first_summaries

2532    This crash occurred in the early afternoon of ...
6209    This two-vehicle crash occurred in a four-legg...
2561    The crash occurred in the eastbound direction ...
Name: SUMMARY_EN, dtype: object

first_words = first_summaries.map(lambda txt: txt.split(" ")[:7])
first_words

2532    [This, crash, occurred, in, the, early, aftern...
6209    [This, two-vehicle, crash, occurred, in, a, fo...
2561    [The, crash, occurred, in, the, eastbound, dir...
Name: SUMMARY_EN, dtype: object

start_of_summaries = first_words.map(lambda txt: " ".join(txt))
start_of_summaries

2532          This crash occurred in the early afternoon
6209    This two-vehicle crash occurred in a four-legged
2561       The crash occurred in the eastbound direction
Name: SUMMARY_EN, dtype: object

Count words in the first summaries

from sklearn.feature_extraction.text import CountVectorizer

vect = CountVectorizer()
counts = vect.fit_transform(start_of_summaries)
vocab = vect.get_feature_names_out()
print(len(vocab), vocab)

13 ['afternoon' 'crash' 'direction' 'early' 'eastbound' 'four' 'in' 'legged'
 'occurred' 'the' 'this' 'two' 'vehicle']

counts

<3x13 sparse matrix of type '<class 'numpy.int64'>'
    with 21 stored elements in Compressed Sparse Row format>

counts.toarray()

array([[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0],
       [0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1],
       [0, 1, 1, 0, 1, 0, 1, 0, 1, 2, 0, 0, 0]])

Encode new sentences to BoW

vect.transform([
    "first car hit second car in a crash",
    "ipad os 16 beta released",
])

<2x13 sparse matrix of type '<class 'numpy.int64'>'
    with 2 stored elements in Compressed Sparse Row format>

vect.transform([
    "first car hit second car in a crash",
    "ipad os 18 beta released",
]).toarray()

array([[0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])

print(vocab)

['afternoon' 'crash' 'direction' 'early' 'eastbound' 'four' 'in' 'legged'
 'occurred' 'the' 'this' 'two' 'vehicle']

Bag of n-grams

vect = CountVectorizer(ngram_range=(1, 2))
counts = vect.fit_transform(start_of_summaries)
vocab = vect.get_feature_names_out()
print(len(vocab), vocab)

27 ['afternoon' 'crash' 'crash occurred' 'direction' 'early'
 'early afternoon' 'eastbound' 'eastbound direction' 'four' 'four legged'
 'in' 'in four' 'in the' 'legged' 'occurred' 'occurred in' 'the'
 'the crash' 'the early' 'the eastbound' 'this' 'this crash' 'this two'
 'two' 'two vehicle' 'vehicle' 'vehicle crash']

counts.toarray()

array([[1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,
        0, 0, 0, 0, 0],
       [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,
        1, 1, 1, 1, 1],
       [0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 2, 1, 0, 1, 0, 0,
        0, 0, 0, 0, 0]])

See: Google Books Ngram Viewer

TF-IDF

Stands for term frequency-inverse document frequency.

Infographic explaining TF-IDF

Bag Of Words

Lecture Outline

• Natural Language Processing

• Car Crash Police Reports

• Text Vectorisation

• Bag Of Words

• Limiting The Vocabulary

• Intelligently Limit The Vocabulary

• Word Embeddings

• Word Embeddings II

• Car Crash NLP Part II

Count words in all the summaries

vect = CountVectorizer()
vect.fit(X_train["SUMMARY_EN"])
vocab = list(vect.get_feature_names_out())
len(vocab)

18866

vocab[:5], vocab[len(vocab)//2:(len(vocab)//2 + 5)], vocab[-5:]

(['00', '000', '000lbs', '003', '005'],
 ['swinger', 'swinging', 'swipe', 'swiped', 'swiping'],
 ['zorcor', 'zotril', 'zx2', 'zx5', 'zyrtec'])

Create the X matrices

def vectorise_dataset(X, vect, txt_col="SUMMARY_EN", dataframe=False):
    X_vects = vect.transform(X[txt_col]).toarray()
    X_other = X.drop(txt_col, axis=1)

    if not dataframe:
        return np.concatenate([X_vects, X_other], axis=1)                           
    else:
        # Add column names and indices to the combined dataframe.
        vocab = list(vect.get_feature_names_out())
        X_vects_df = pd.DataFrame(X_vects, columns=vocab, index=X.index)
        return pd.concat([X_vects_df, X_other], axis=1)

X_train_bow = vectorise_dataset(X_train, vect)
X_val_bow = vectorise_dataset(X_val, vect)
X_test_bow = vectorise_dataset(X_test, vect)

Check the input matrix

vectorise_dataset(X_train, vect, dataframe=True)

	00	000	000lbs	003	005	007	00am	00pm	00tydo2	01	...	zx5	zyrtec	WEATHER1	WEATHER2	WEATHER3	WEATHER4	WEATHER5	WEATHER6	WEATHER7	WEATHER8
2532	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
6209	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2561	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6882	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
206	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
6356	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

4169 rows × 18874 columns

Make a simple dense model

num_features = X_train_bow.shape[1]
num_cats = 3 # 1, 2, 3+ vehicles

def build_model(num_features, num_cats):
    random.seed(42)
    
    model = Sequential([
        Input((num_features,)),
        Dense(100, activation="relu"),
        Dense(num_cats, activation="softmax")
    ])
    
    topk = SparseTopKCategoricalAccuracy(k=2, name="topk")
    model.compile("adam", "sparse_categorical_crossentropy",
        metrics=["accuracy", topk])
    
    return model

Inspect the model

model = build_model(num_features, num_cats)
model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 100)            │     1,887,500 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 3)              │           303 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 1,887,803 (7.20 MB)

 Trainable params: 1,887,803 (7.20 MB)

 Non-trainable params: 0 (0.00 B)

Fit & evaluate the model

es = EarlyStopping(patience=1, restore_best_weights=True,
    monitor="val_accuracy", verbose=2)
%time hist = model.fit(X_train_bow, y_train, epochs=10, \
    callbacks=[es], validation_data=(X_val_bow, y_val), verbose=0);

Epoch 5: early stopping
Restoring model weights from the end of the best epoch: 4.
CPU times: user 38.4 s, sys: 1.5 s, total: 39.9 s
Wall time: 4.32 s

model.evaluate(X_train_bow, y_train, verbose=0)

[0.002541527384892106, 1.0, 1.0]

model.evaluate(X_val_bow, y_val, verbose=0)

[2.776606559753418, 0.9453237652778625, 0.9949640035629272]

As this happens to be the best in validation set, we can check the performance on the test set.

model.evaluate(X_test_bow, y_test, verbose=0)

[0.1902949959039688, 0.9374100565910339, 0.9971222877502441]

Limiting The Vocabulary

Lecture Outline

• Natural Language Processing

• Car Crash Police Reports

• Text Vectorisation

• Bag Of Words

• Limiting The Vocabulary

• Intelligently Limit The Vocabulary

• Word Embeddings

• Word Embeddings II

• Car Crash NLP Part II

The max_features value

vect = CountVectorizer(max_features=10)
vect.fit(X_train["SUMMARY_EN"])
vocab = vect.get_feature_names_out()
len(vocab)

10

print(vocab)

['and' 'driver' 'for' 'in' 'lane' 'of' 'the' 'to' 'vehicle' 'was']

What is left?

for i in range(3):
    sentence = X_train["SUMMARY_EN"].iloc[i]
    for word in sentence.split(" ")[:10]:
        word_or_qn = word if word in vocab else "?"
        print(word_or_qn, end=" ")
    print("\n")

? ? ? in the ? ? of ? ? 

? ? ? ? in ? ? ? ? ? 

? ? ? in the ? ? of ? ? 

for i in range(3):
    sentence = X_train["SUMMARY_EN"].iloc[i]
    num_words = 0
    for word in sentence.split(" "):
        if word in vocab:
            print(word, end=" ")
            num_words += 1
        if num_words == 10:
            break
    print("\n")

in the of in the of of was and was 

in and of in and for the of the and 

in the of to was was of was was and 

Remove stop words

vect = CountVectorizer(max_features=10, stop_words="english")
vect.fit(X_train["SUMMARY_EN"])
vocab = vect.get_feature_names_out()
len(vocab)

10

print(vocab)

['coded' 'crash' 'critical' 'driver' 'event' 'intersection' 'lane' 'left'
 'roadway' 'vehicle']

for i in range(3):
    sentence = X_train["SUMMARY_EN"].iloc[i]
    num_words = 0
    for word in sentence.split(" "):
        if word in vocab:
            print(word, end=" ")
            num_words += 1
        if num_words == 10:
            break
    print("\n")

crash intersection roadway roadway roadway intersection lane lane intersection driver 

crash roadway left roadway roadway roadway lane lane roadway crash 

crash vehicle left left vehicle driver vehicle lane lane coded 

Keep 1,000 most frequent words

vect = CountVectorizer(max_features=1_000, stop_words="english")
vect.fit(X_train["SUMMARY_EN"])
vocab = vect.get_feature_names_out()
len(vocab)

1000

print(vocab[:5], vocab[len(vocab)//2:(len(vocab)//2 + 5)], vocab[-5:])

['10' '105' '113' '12' '15'] ['interruption' 'intersected' 'intersecting' 'intersection' 'interstate'] ['year' 'years' 'yellow' 'yield' 'zone']

Create the X matrices:

X_train_bow = vectorise_dataset(X_train, vect)
X_val_bow = vectorise_dataset(X_val, vect)
X_test_bow = vectorise_dataset(X_test, vect)

What is left?

for i in range(10):
    sentence = X_train["SUMMARY_EN"].iloc[i]
    num_words = 0
    for word in sentence.split(" "):
        if word in vocab:
            print(word, end=" ")
            num_words += 1
        if num_words == 10:
            break
    print("\n")

crash occurred early afternoon weekday middle suburban intersection consisted lanes 

crash occurred roadway level consists lanes direction center left turn 

crash occurred eastbound direction entrance ramp right curved road uphill 

crash occurred straight roadway consists lanes direction center left turn 

collision occurred evening hours crash occurred level bituminous roadway residential 

vehicle crash occurred daylight location lane undivided left curved downhill 

vehicle crash occurred early morning daylight hours roadway traffic roadway 

crash occurred northbound lanes northbound southbound slightly street curved posted 

crash occurred eastbound lanes access highway weekend roadway consisted lanes 

collision occurred intersection north south traffic controlled stop roadways left 

Check the input matrix

vectorise_dataset(X_train, vect, dataframe=True)

	10	105	113	12	15	150	16	17	18	180	...	yield	zone	WEATHER1	WEATHER2	WEATHER3	WEATHER4	WEATHER5	WEATHER6	WEATHER7	WEATHER8
2532	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
6209	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2561	1	0	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6882	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
206	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
6356	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

4169 rows × 1008 columns

Make & inspect the model

num_features = X_train_bow.shape[1]
model = build_model(num_features, num_cats)
model.summary()

Model: "sequential_1"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense_2 (Dense)                 │ (None, 100)            │       100,900 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_3 (Dense)                 │ (None, 3)              │           303 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 101,203 (395.32 KB)

 Trainable params: 101,203 (395.32 KB)

 Non-trainable params: 0 (0.00 B)

Fit & evaluate the model

es = EarlyStopping(patience=1, restore_best_weights=True,
    monitor="val_accuracy", verbose=2)
%time hist = model.fit(X_train_bow, y_train, epochs=10, \
    callbacks=[es], validation_data=(X_val_bow, y_val), verbose=0);

Epoch 3: early stopping
Restoring model weights from the end of the best epoch: 2.
CPU times: user 1.86 s, sys: 327 ms, total: 2.19 s
Wall time: 1.11 s

model.evaluate(X_train_bow, y_train, verbose=0)

[0.1021684780716896, 0.9815303683280945, 0.9990405440330505]

model.evaluate(X_val_bow, y_val, verbose=0)

[2.4335882663726807, 0.9381294846534729, 0.9942445755004883]

Intelligently Limit The Vocabulary

Lecture Outline

• Natural Language Processing

• Car Crash Police Reports

• Text Vectorisation

• Bag Of Words

• Limiting The Vocabulary

• Intelligently Limit The Vocabulary

• Word Embeddings

• Word Embeddings II

• Car Crash NLP Part II

Keep 1,000 most frequent words

vect = CountVectorizer(max_features=1_000, stop_words="english")
vect.fit(X_train["SUMMARY_EN"])
vocab = vect.get_feature_names_out()
len(vocab)

1000

print(vocab[:5], vocab[len(vocab)//2:(len(vocab)//2 + 5)], vocab[-5:])

['10' '105' '113' '12' '15'] ['interruption' 'intersected' 'intersecting' 'intersection' 'interstate'] ['year' 'years' 'yellow' 'yield' 'zone']

Install spacy

!pip install spacy
!python -m spacy download en_core_web_sm

import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("Apple is looking at buying U.K. startup for $1 billion")
for token in doc:
    print(token.text, token.pos_, token.dep_, token.lemma_)

Apple PROPN nsubj Apple
is AUX aux be
looking VERB ROOT look
at ADP prep at
buying VERB pcomp buy
U.K. PROPN dobj U.K.
startup NOUN dobj startup
for ADP prep for
$ SYM quantmod $
1 NUM compound 1
billion NUM pobj billion

Stemming

“Stemming refers to the process of removing suffixes and reducing a word to some base form such that all different variants of that word can be represented by the same form (e.g., “car” and “cars” are both reduced to “car”). This is accomplished by applying a fixed set of rules (e.g., if the word ends in “-es,” remove “-es”). More such examples are shown in Figure 2-7. Although such rules may not always end up in a linguistically correct base form, stemming is commonly used in search engines to match user queries to relevant documents and in text classification to reduce the feature space to train machine learning models.”

Lemmatization

“Lemmatization is the process of mapping all the different forms of a word to its base word, or lemma. While this seems close to the definition of stemming, they are, in fact, different. For example, the adjective “better,” when stemmed, remains the same. However, upon lemmatization, this should become “good,” as shown in Figure 2-7. Lemmatization requires more linguistic knowledge, and modeling and developing efficient lemmatizers remains an open problem in NLP research even now.”

Stemming and lemmatizing

Examples of stemming and lemmatization

Original: “The striped bats are hanging on their feet for best”

Stemmed: “the stripe bat are hang on their feet for best”

Lemmatized: “the stripe bat be hang on their foot for good”

Examples

Stemmed

organization -> organ

civilization -> civil

information -> inform

consultant -> consult

Lemmatized

[‘I’, ‘will’, ‘be’, ‘back’, ‘.’]

I’ll be back (Terminator)

[‘here’, ‘be’, ‘look’, ‘at’, ‘you’, ‘,’, ‘kid’, ‘.’]

“Here’s looking at you, kid.” (Casablanca)

Lemmatize the text

def lemmatize(txt):
    doc = nlp(txt)
    good_tokens = [token.lemma_.lower() for token in doc \
        if not token.like_num and \
           not token.is_punct and \
           not token.is_space and \
           not token.is_currency and \
           not token.is_stop]
    return " ".join(good_tokens)

test_str = "Incident at 100kph and '10 incidents -13.3%' are incidental?\t $5"
lemmatize(test_str)

'incident 100kph incident incidental'

test_str = "I interviewed 5-years ago, 150 interviews every year at 10:30 are.."
lemmatize(test_str)

'interview year ago interview year 10:30'

Apply to the whole dataset

df["SUMMARY_EN_LEMMA"] = df["SUMMARY_EN"].map(lemmatize)

weather_cols = [f"WEATHER{i}" for i in range(1, 9)]
features = df[["SUMMARY_EN_LEMMA"] + weather_cols]

X_main, X_test, y_main, y_test = \
    train_test_split(features, target, test_size=0.2, random_state=1)

# As 0.25 x 0.8 = 0.2
X_train, X_val, y_train, y_val = \
    train_test_split(X_main, y_main, test_size=0.25, random_state=1)

X_train.shape, X_val.shape, X_test.shape

((4169, 9), (1390, 9), (1390, 9))

What is left?

print("Original:", df["SUMMARY_EN"].iloc[0][:250])

Original: V6357885318682, a 2000 Pontiac Montana minivan, made a left turn from a private driveway onto a northbound 5-lane two-way, dry asphalt roadway on a downhill grade.  The posted speed limit on this roadway was 80 kmph (50 MPH). V6357885318682 entered t

print("Lemmatized:", df["SUMMARY_EN_LEMMA"].iloc[0][:250])

Lemmatized: v6357885318682 pontiac montana minivan left turn private driveway northbound lane way dry asphalt roadway downhill grade post speed limit roadway kmph mph v6357885318682 enter roadway cross southbound lane enter northbound lane left turn lane way int

print("Original:", df["SUMMARY_EN"].iloc[1][:250])

Original: The crash occurred in the eastbound lane of a two-lane, two-way asphalt roadway on level grade.  The conditions were daylight and wet with cloudy skies in the early afternoon on a weekday.  
 
 V342542243, a 1995 Chevrolet Lumina was traveling eastbou

print("Lemmatized:", df["SUMMARY_EN_LEMMA"].iloc[1][:250])

Lemmatized: crash occur eastbound lane lane way asphalt roadway level grade condition daylight wet cloudy sky early afternoon weekday v342542243 chevrolet lumina travel eastbound v342542269 chevrolet trailblazer travel eastbound roadway v342542269 attempt left h

Keep 1,000 most frequent lemmas

vect = CountVectorizer(max_features=1_000, stop_words="english")
vect.fit(X_train["SUMMARY_EN_LEMMA"])
vocab = vect.get_feature_names_out()
len(vocab)

1000

print(vocab[:5], vocab[len(vocab)//2:(len(vocab)//2 + 5)], vocab[-5:])

['10' '150' '48kmph' '4x4' '56kmph'] ['let' 'level' 'lexus' 'license' 'light'] ['yaw' 'year' 'yellow' 'yield' 'zone']

Create the X matrices:

X_train_bow = vectorise_dataset(X_train, vect, "SUMMARY_EN_LEMMA")
X_val_bow = vectorise_dataset(X_val, vect, "SUMMARY_EN_LEMMA")
X_test_bow = vectorise_dataset(X_test, vect, "SUMMARY_EN_LEMMA")

Check the input matrix

vectorise_dataset(X_train, vect, "SUMMARY_EN_LEMMA", dataframe=True)

	10	150	48kmph	4x4	56kmph	64kmph	72kmph	ability	able	accelerate	...	yield	zone	WEATHER1	WEATHER2	WEATHER3	WEATHER4	WEATHER5	WEATHER6	WEATHER7	WEATHER8
2532	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
6209	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2561	0	0	0	0	1	1	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6882	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
206	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
6356	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

4169 rows × 1008 columns

Make & inspect the model

num_features = X_train_bow.shape[1]
model = build_model(num_features, num_cats)
model.summary()

Model: "sequential_2"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense_4 (Dense)                 │ (None, 100)            │       100,900 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_5 (Dense)                 │ (None, 3)              │           303 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 101,203 (395.32 KB)

 Trainable params: 101,203 (395.32 KB)

 Non-trainable params: 0 (0.00 B)

Fit & evaluate the model

es = EarlyStopping(patience=1, restore_best_weights=True,
    monitor="val_accuracy", verbose=2)
%time hist = model.fit(X_train_bow, y_train, epochs=10, \
    callbacks=[es], validation_data=(X_val_bow, y_val), verbose=0);

Epoch 3: early stopping
Restoring model weights from the end of the best epoch: 2.
CPU times: user 2.19 s, sys: 321 ms, total: 2.52 s
Wall time: 1.23 s

model.evaluate(X_train_bow, y_train, verbose=0)

[0.09055039286613464, 0.9851283431053162, 0.9990405440330505]

model.evaluate(X_val_bow, y_val, verbose=0)

[3.8409152030944824, 0.9402877688407898, 0.9928057789802551]

Word Embeddings

Lecture Outline

• Natural Language Processing

• Car Crash Police Reports

• Text Vectorisation

• Bag Of Words

• Limiting The Vocabulary

• Intelligently Limit The Vocabulary

• Word Embeddings

• Word Embeddings II

• Car Crash NLP Part II

Overview

In order for deep learning models to process language, we need to supply that language to the model in a way it can digest, i.e. a quantitative representation such as a 2-D matrix of numerical values.

Popular methods for converting text into numbers include:

• One-hot encoding
• Bag of words
• TF-IDF
• Word vectors (transfer learning)

Assigning Numbers

Word Vectors

• One-hot representations capture word ‘existence’ only, whereas word vectors capture information about word meaning as well as location.
• This enables deep learning NLP models to automatically learn linguistic features.
• Word2Vec & GloVe are popular algorithms for generating word embeddings (i.e. word vectors).

Word Vectors

Illustrative word vectors.

• Overarching concept is to assign each word within a corpus to a particular, meaningful location within a multidimensional space called the vector space.
• Initially each word is assigned to a random location.
• BUT by considering the words that tend to be used around a given word within the corpus, the locations of the words shift.

Remember this diagram?

Embeddings will gradually improve during training.

Word2Vec

Key idea: You’re known by the company you keep.

Two algorithms are used to calculate embeddings:

• Continuous bag of words: uses the context words to predict the target word
• Skip-gram: uses the target word to predict the context words

Predictions are made using a neural network with one hidden layer. Through backpropagation, we update a set of “weights” which become the word vectors.

Word2Vec training methods

Continuous bag of words is a center word prediction task

Skip-gram is a neighbour word prediction task

Suggested viewing

Computerphile (2019), Vectoring Words (Word Embeddings), YouTube (16 mins).

The skip-gram network

The skip-gram model. Both the input vector \boldsymbol{x} and the output \boldsymbol{y} are one-hot encoded word representations. The hidden layer is the word embedding of size N.

Word Vector Arithmetic

Relationships between words becomes vector math.

You remember vectors, right?

• E.g., if we calculate the direction and distance between the coordinates of the words Paris and France, and trace this direction and distance from London, we should be close to the word England.

Illustrative word vector arithmetic

Screenshot from Word2viz

Word Embeddings II

Lecture Outline

• Natural Language Processing

• Car Crash Police Reports

• Text Vectorisation

• Bag Of Words

• Limiting The Vocabulary

• Intelligently Limit The Vocabulary

• Word Embeddings

• Word Embeddings II

• Car Crash NLP Part II

Pretrained word embeddings

!pip install gensim

Load word2vec embeddings trained on Google News:

import gensim.downloader as api
wv = api.load('word2vec-google-news-300')

When run for the first time, that downloads a huge file:

gensim_dir = Path("~/gensim-data/").expanduser()
[str(p) for p in gensim_dir.iterdir()]

['/home/plaub/gensim-data/information.json',
 '/home/plaub/gensim-data/word2vec-google-news-300']

next(gensim_dir.glob("*/*.gz")).stat().st_size / 1024**3

1.6238203644752502

f"The size of the vocabulary is {len(wv)}"

'The size of the vocabulary is 3000000'

Treat wv like a dictionary

wv["pizza"]

array([-1.26e-01,  2.54e-02,  1.67e-01,  5.51e-01, -7.67e-02,  1.29e-01,
        1.03e-01, -3.95e-04,  1.22e-01,  4.32e-02,  1.73e-01, -6.84e-02,
        3.42e-01,  8.40e-02,  6.69e-02,  2.68e-01, -3.71e-02, -5.57e-02,
        1.81e-01,  1.90e-02, -5.08e-02,  9.03e-03,  1.77e-01,  6.49e-02,
       -6.25e-02, -9.42e-02, -9.72e-02,  4.00e-01,  1.15e-01,  1.03e-01,
       -1.87e-02, -2.70e-01,  1.81e-01,  1.25e-01, -3.17e-02, -5.49e-02,
        3.46e-01, -1.57e-02,  1.82e-05,  2.07e-01, -1.26e-01, -2.83e-01,
        2.00e-01,  8.35e-02, -4.74e-02, -3.11e-02, -2.62e-01,  1.70e-01,
       -2.03e-02,  1.53e-01, -1.21e-01,  3.75e-01, -5.69e-02, -4.76e-03,
       -1.95e-01, -2.03e-01,  3.01e-01, -1.01e-01, -3.18e-01, -9.03e-02,
       -1.19e-01,  1.95e-01, -8.79e-02,  1.58e-01,  1.52e-02, -1.60e-01,
       -3.30e-01, -4.67e-01,  1.69e-01,  2.23e-02,  1.55e-01,  1.08e-01,
       -3.56e-02,  9.13e-02, -8.69e-02, -1.20e-01, -3.09e-01, -2.61e-02,
       -7.23e-02, -4.80e-01,  3.78e-02, -1.36e-01, -1.03e-01, -2.91e-01,
       -1.93e-01, -4.22e-01, -1.06e-01,  3.55e-01,  1.67e-01, -3.63e-03,
       -7.42e-02, -3.22e-01, -7.52e-02, -8.25e-02, -2.91e-01, -1.26e-01,
        1.68e-02,  5.00e-02,  1.28e-01, -7.42e-02, -1.31e-01, -2.46e-01,
        6.49e-02,  1.53e-01,  2.60e-01, -1.05e-01,  3.57e-01, -4.30e-02,
       -1.58e-01,  8.20e-02, -5.98e-02, -2.34e-01, -3.22e-01, -1.26e-01,
        5.40e-02, -1.88e-01,  1.36e-01, -6.59e-02,  8.36e-03, -1.85e-01,
       -2.97e-01, -1.85e-01, -4.74e-02, -1.06e-01, -6.93e-02,  3.83e-02,
       -3.20e-02,  3.64e-02, -1.20e-01,  1.77e-01, -1.16e-01,  1.99e-02,
        8.64e-02,  6.08e-02, -1.41e-01,  3.30e-01,  1.94e-01, -1.56e-01,
        3.93e-01,  1.81e-03,  7.28e-02, -2.54e-01, -3.54e-02,  2.87e-03,
       -1.73e-01,  9.77e-03, -1.56e-02,  3.23e-03, -1.70e-01,  1.55e-01,
        7.18e-02,  4.10e-01, -2.11e-01,  1.32e-01,  7.63e-03,  4.79e-02,
       -4.54e-02,  7.32e-02, -4.06e-01, -2.06e-02, -4.04e-01, -1.01e-01,
       -2.03e-01,  1.55e-01, -1.89e-01,  6.59e-02,  6.54e-02, -2.05e-01,
        5.47e-02, -3.06e-02, -1.54e-01, -2.62e-01,  3.81e-03, -8.20e-02,
       -3.20e-01,  2.84e-02,  2.70e-01,  1.74e-01, -1.67e-01,  2.23e-01,
        6.35e-02, -1.96e-01,  1.46e-01, -1.56e-02,  2.60e-02, -6.30e-02,
        2.94e-02,  3.28e-01, -4.69e-02, -1.52e-01,  6.98e-02,  3.18e-01,
       -1.08e-01,  3.66e-02, -1.99e-01,  1.64e-03,  6.41e-03, -1.47e-01,
       -6.25e-02, -4.36e-03, -2.75e-01,  8.54e-02, -5.00e-02, -3.12e-01,
       -1.34e-01, -1.99e-01,  5.18e-02, -9.28e-02, -2.40e-01, -7.86e-02,
       -1.54e-01, -6.64e-02, -1.97e-01,  1.77e-01, -1.57e-01, -1.63e-01,
        6.01e-02, -5.86e-02, -2.23e-01, -6.59e-02, -9.38e-02, -4.14e-01,
        2.56e-01, -1.77e-01,  2.52e-01,  1.48e-01, -1.04e-01, -8.61e-03,
       -1.23e-01, -9.23e-02,  4.42e-02, -1.71e-01, -1.98e-01,  1.92e-01,
        2.85e-01, -4.35e-02,  1.08e-01, -5.37e-02, -2.10e-02,  1.46e-01,
        3.83e-01,  2.32e-02, -8.84e-02,  7.32e-02, -1.01e-01, -1.06e-01,
        4.12e-01,  2.11e-01,  2.79e-01, -2.09e-02,  2.07e-01,  9.81e-02,
        2.39e-01,  7.67e-02,  2.02e-01, -6.08e-02, -2.64e-03, -1.84e-01,
       -1.57e-02, -3.20e-01,  9.03e-02,  1.02e-01, -4.96e-01, -9.72e-02,
       -8.11e-02, -1.81e-01, -1.46e-01,  8.64e-02, -2.04e-01, -2.02e-01,
       -5.47e-02,  2.54e-01,  2.09e-02, -1.16e-01,  2.02e-01, -8.06e-02,
       -1.05e-01, -7.96e-02,  1.97e-02, -2.49e-01,  1.31e-01,  2.89e-01,
       -2.26e-01,  4.55e-01, -2.73e-01, -2.58e-01, -3.15e-02,  4.04e-01,
       -2.68e-01,  2.89e-01, -1.84e-01, -1.48e-01, -1.07e-01,  1.28e-01,
        5.47e-01, -8.69e-02, -1.48e-02,  6.98e-02, -8.50e-02, -1.55e-01],
      dtype=float32)

len(wv["pizza"])

300

Find nearby word vectors

wv.most_similar("Python")

[('Jython', 0.6152505874633789),
 ('Perl_Python', 0.5710949897766113),
 ('IronPython', 0.5704679489135742),
 ('scripting_languages', 0.5695090889930725),
 ('PHP_Perl', 0.5687724947929382),
 ('Java_Python', 0.5681070685386658),
 ('PHP', 0.5660915970802307),
 ('Python_Ruby', 0.5632461905479431),
 ('Visual_Basic', 0.5603480339050293),
 ('Perl', 0.5530891418457031)]

wv.similarity("Python", "Java")

0.46189708

wv.similarity("Python", "sport")

0.08406468

wv.similarity("Python", "R")

0.066954285

What does ‘similarity’ mean?

The ‘similarity’ scores

wv.similarity("Sydney", "Melbourne")

0.8613987

are normally based on cosine distance.

x = wv["Sydney"]
y = wv["Melbourne"]
x.dot(y) / (np.linalg.norm(x) * np.linalg.norm(y))

0.86139864

wv.similarity("Sydney", "Aarhus")

0.19079602

Weng’s GoT Word2Vec

In the GoT word embedding space, the top similar words to “king” and “queen” are:

model.most_similar("king")

('kings', 0.897245) 
('baratheon', 0.809675) 
('son', 0.763614)
('robert', 0.708522)
('lords', 0.698684)
('joffrey', 0.696455)
('prince', 0.695699)
('brother', 0.685239)
('aerys', 0.684527)
('stannis', 0.682932)

model.most_similar("queen")

('cersei', 0.942618)
('joffrey', 0.933756)
('margaery', 0.931099)
('sister', 0.928902)
('prince', 0.927364)
('uncle', 0.922507)
('varys', 0.918421)
('ned', 0.917492)
('melisandre', 0.915403)
('robb', 0.915272)

Combining word vectors

You can summarise a sentence by averaging the individual word vectors.

sv = (wv["Melbourne"] + wv["has"] + wv["better"] + wv["coffee"]) / 4
len(sv), sv[:5]

(300, array([-0.08, -0.11, -0.16,  0.24,  0.06], dtype=float32))

As it turns out, averaging word embeddings is a surprisingly effective way to create word embeddings. It’s not perfect (as you’ll see), but it does a strong job of capturing what you might perceive to be complex relationships between words.

Recipe recommender

Recipes are the average of the word vectors of the ingredients.

Nearest neighbours used to classify new recipes as potentially delicious.

Analogies with word vectors

Obama is to America as ___ is to Australia.

\text{Obama} - \text{America} + \text{Australia} = ?

wv.most_similar(positive=["Obama", "Australia"], negative=["America"])

[('Mr_Rudd', 0.6151423454284668),
 ('Prime_Minister_Julia_Gillard', 0.6045385003089905),
 ('Prime_Minister_Kevin_Rudd', 0.5982581973075867),
 ('Kevin_Rudd', 0.5627648830413818),
 ('Ms_Gillard', 0.5517690777778625),
 ('Opposition_Leader_Kevin_Rudd', 0.5298037528991699),
 ('Mr_Beazley', 0.5259249210357666),
 ('Gillard', 0.5250653624534607),
 ('NARDA_GILMORE', 0.5203536748886108),
 ('Mr_Downer', 0.5150347948074341)]

Testing more associations

wv.most_similar(positive=["France", "London"], negative=["Paris"])

[('Britain', 0.7368935346603394),
 ('UK', 0.6637030839920044),
 ('England', 0.6119861602783203),
 ('United_Kingdom', 0.6067784428596497),
 ('Great_Britain', 0.5870823860168457),
 ('Britian', 0.5852951407432556),
 ('Scotland', 0.5410018563270569),
 ('British', 0.5318332314491272),
 ('Europe', 0.5307435989379883),
 ('East_Midlands', 0.5230222344398499)]

Quickly get to bad associations

wv.most_similar(positive=["King", "woman"], negative=["man"])

[('Queen', 0.5515626668930054),
 ('Oprah_BFF_Gayle', 0.47597548365592957),
 ('Geoffrey_Rush_Exit', 0.46460166573524475),
 ('Princess', 0.4533674716949463),
 ('Yvonne_Stickney', 0.4507041573524475),
 ('L._Bonauto', 0.4422135353088379),
 ('gal_pal_Gayle', 0.4408389925956726),
 ('Alveda_C.', 0.4402790665626526),
 ('Tupou_V.', 0.4373864233493805),
 ('K._Letourneau', 0.4351031482219696)]

wv.most_similar(positive=["computer_programmer", "woman"], negative=["man"])

[('homemaker', 0.5627118945121765),
 ('housewife', 0.5105047225952148),
 ('graphic_designer', 0.505180299282074),
 ('schoolteacher', 0.497949481010437),
 ('businesswoman', 0.493489146232605),
 ('paralegal', 0.49255111813545227),
 ('registered_nurse', 0.4907974898815155),
 ('saleswoman', 0.4881627559661865),
 ('electrical_engineer', 0.4797725975513458),
 ('mechanical_engineer', 0.4755399227142334)]

Bias in NLP models

The Verge (2016), Twitter taught Microsoft’s AI chatbot to be a racist a****** in less than a day.

… there are serious questions to answer, like how are we going to teach AI using public data without incorporating the worst traits of humanity? If we create bots that mirror their users, do we care if their users are human trash? There are plenty of examples of technology embodying — either accidentally or on purpose — the prejudices of society, and Tay’s adventures on Twitter show that even big corporations like Microsoft forget to take any preventative measures against these problems.

The library cheats a little bit

wv.similar_by_vector(wv["computer_programmer"] - wv["man"] + wv["woman"])

[('computer_programmer', 0.910581111907959),
 ('homemaker', 0.5771316289901733),
 ('schoolteacher', 0.5500192046165466),
 ('graphic_designer', 0.5464698672294617),
 ('mechanical_engineer', 0.539836585521698),
 ('electrical_engineer', 0.5337055325508118),
 ('housewife', 0.5274525284767151),
 ('programmer', 0.5096209049224854),
 ('businesswoman', 0.5029540657997131),
 ('keypunch_operator', 0.4974639415740967)]

To get the ‘nice’ analogies, the .most_similar ignores the input words as possible answers.

# ignore (don't return) keys from the input
result = [
    (self.index_to_key[sim + clip_start], float(dists[sim]))
    for sim in best if (sim + clip_start) not in all_keys
]

Car Crash NLP Part II

Lecture Outline

• Natural Language Processing

• Car Crash Police Reports

• Text Vectorisation

• Bag Of Words

• Limiting The Vocabulary

• Intelligently Limit The Vocabulary

• Word Embeddings

• Word Embeddings II

• Car Crash NLP Part II

Predict injury severity

features = df["SUMMARY_EN"]
target = LabelEncoder().fit_transform(df["INJSEVB"])

X_main, X_test, y_main, y_test = \
    train_test_split(features, target, test_size=0.2, random_state=1)
X_train, X_val, y_train, y_val = \
    train_test_split(X_main, y_main, test_size=0.25, random_state=1)
X_train.shape, X_val.shape, X_test.shape

((4169,), (1390,), (1390,))

Using Keras TextVectorization

max_tokens = 1_000
vect = layers.TextVectorization(
    max_tokens=max_tokens,
    output_mode="tf_idf",
    standardize="lower_and_strip_punctuation",
)

vect.adapt(X_train)
vocab = vect.get_vocabulary()

X_train_txt = vect(X_train)
X_val_txt = vect(X_val)
X_test_txt = vect(X_test)

print(vocab[:50])

['[UNK]', 'the', 'was', 'a', 'to', 'of', 'and', 'in', 'driver', 'for', 'this', 'vehicle', 'critical', 'lane', 'he', 'on', 'with', 'that', 'left', 'roadway', 'coded', 'she', 'event', 'crash', 'not', 'at', 'intersection', 'traveling', 'right', 'precrash', 'as', 'from', 'were', 'by', 'had', 'reason', 'his', 'side', 'is', 'front', 'her', 'traffic', 'an', 'it', 'two', 'speed', 'stated', 'one', 'occurred', 'no']

The TF-IDF vectors

pd.DataFrame(X_train_txt, columns=vocab, index=X_train.index)

	[UNK]	the	was	a	to	of	and	in	driver	for	...	encroaching	closely	ordinarily	locked	history	fourleg	determined	box	altima	above
2532	121.857979	42.274662	10.395409	10.395409	11.785541	8.323526	8.323526	9.775118	3.489896	4.168983	...	0.0	0.0	0.00000	0.0	0.0	0.0	0.0	0.0	0.0	0.0
6209	72.596237	17.325682	10.395409	5.544218	4.159603	5.549018	7.629900	4.887559	4.187876	6.253474	...	0.0	0.0	0.00000	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2561	124.450699	30.493198	15.246599	11.088436	9.012472	7.629900	8.323526	2.792891	3.489896	5.558644	...	0.0	0.0	0.00000	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6882	75.188965	20.790817	4.851191	7.623300	9.012472	4.855391	4.161763	2.094668	5.583834	2.084491	...	0.0	0.0	3.61771	0.0	0.0	0.0	0.0	0.0	0.0	0.0
206	147.785202	27.028063	13.167518	6.237246	8.319205	4.855391	6.242645	2.094668	3.489896	9.032796	...	0.0	0.0	0.00000	0.0	0.0	0.0	0.0	0.0	0.0	0.0
6356	75.188965	15.246599	9.702381	8.316327	7.625938	5.549018	7.629900	8.378673	2.791917	5.558644	...	0.0	0.0	0.00000	0.0	0.0	0.0	0.0	0.0	0.0	0.0

4169 rows × 1000 columns

Feed TF-IDF into an ANN

random.seed(42)
tfidf_model = keras.models.Sequential([
    layers.Input((X_train_txt.shape[1],)),
    layers.Dense(250, "relu"),
    layers.Dense(1, "sigmoid")
])

tfidf_model.compile("adam", "binary_crossentropy", metrics=["accuracy"])
tfidf_model.summary()

Model: "sequential_3"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense_6 (Dense)                 │ (None, 250)            │       250,250 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_7 (Dense)                 │ (None, 1)              │           251 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 250,501 (978.52 KB)

 Trainable params: 250,501 (978.52 KB)

 Non-trainable params: 0 (0.00 B)

Fit & evaluate

es = keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True,
    monitor="val_accuracy", verbose=2)

if not Path("tfidf-model.keras").exists():
    tfidf_model.fit(X_train_txt, y_train, epochs=1_000, callbacks=es,
        validation_data=(X_val_txt, y_val), verbose=0)
    tfidf_model.save("tfidf-model.keras")
else:
    tfidf_model = keras.models.load_model("tfidf-model.keras")

tfidf_model.evaluate(X_train_txt, y_train, verbose=0, batch_size=1_000)

[0.11705566942691803, 0.9575437903404236]

tfidf_model.evaluate(X_val_txt, y_val, verbose=0, batch_size=1_000)

[0.3212660849094391, 0.8848921060562134]

Keep text as sequence of tokens

max_length = 500
max_tokens = 1_000
vect = layers.TextVectorization(
    max_tokens=max_tokens,
    output_sequence_length=max_length,
    standardize="lower_and_strip_punctuation",
)

vect.adapt(X_train)
vocab = vect.get_vocabulary()

X_train_txt = vect(X_train)
X_val_txt = vect(X_val)
X_test_txt = vect(X_test)

print(vocab[:50])

['', '[UNK]', 'the', 'was', 'a', 'to', 'of', 'and', 'in', 'driver', 'for', 'this', 'vehicle', 'critical', 'lane', 'he', 'on', 'with', 'that', 'left', 'roadway', 'coded', 'she', 'event', 'crash', 'not', 'at', 'intersection', 'traveling', 'right', 'precrash', 'as', 'from', 'were', 'by', 'had', 'reason', 'his', 'side', 'is', 'front', 'her', 'traffic', 'an', 'it', 'two', 'speed', 'stated', 'one', 'occurred']

A sequence of integers

X_train_txt[0]

<tf.Tensor: shape=(500,), dtype=int64, numpy=
array([ 11,  24,  49,   8,   2, 253, 219,   6,   4, 165,   8,   2, 410,
         6,   4, 564, 971,  27,   2,  27, 568,   6,   4, 192,   1,  45,
        51, 208,  65, 235,  54,  14,  20, 867,  34,  43, 183,   1,  45,
        51, 208,  65, 235,  54,  14,  20, 178,  34,   4, 676,   1,  42,
       237,   2, 153, 192,  20,   3, 107,   7,  75,  17,   4, 612, 441,
       549,   2,  88,  46,   3, 207,  63, 185,  55,   2,  42, 243,   3,
       400,   7,  58,  33,  50, 172, 251,  84,  26,   2,  60,   6,   2,
        24,   1,   4, 402, 970,   1,   1,   3,  68,  26,   2,  27,  94,
       118,   8,  14, 101, 311,  10,   2, 237,   5, 422, 269,  44, 154,
        54,  19,   1,   4, 308, 342,   1,   3,  79,   8,  14,  45, 159,
         2, 121,  27, 190,  44, 598,   5, 325,  75,  70,   2, 105, 189,
       231,   1, 241,  81,  19,  31,   1, 193,   2,  54,  81,   9, 134,
         4, 174,  12,  17,   1, 390,   1, 159,   2,  27,  32,   2, 119,
         1,  68,   8,   2, 410,   6,   2,  27,   8,   1,   5,   2, 159,
       174,  12,   1, 168,   2,  27,   7,  69,   2,  40,   6,   1,  17,
        81,  40,  19, 246,  73,  83,  64,   5, 129,  56,   8,   2,  27,
         7,  33,  73,  71,  57,   5,  82,   2,   9,   6,   1,   4,   1,
        59, 382,   5, 113,   8, 276, 258,   1, 317, 928, 284,  10, 784,
       294, 462, 483,   7,   1,  15,   3,  16,  37, 112,   5, 677, 144,
         1,  26,   2,  60,   6,   2,  24,  15,  47,  18,  70,   2, 105,
       429,  15,  35, 448,   1,   5, 493,  37,  54,  62,  68,  25,   1,
        33,   5, 325,  70,  15, 134,   2, 174, 232, 406,  15, 341, 134,
         1, 691,   2,  27,   7,  15,   1,  10,  93,  15,   3,  25, 216,
         8,   2,  24,   2,  13,  30,  23,  10,   1,   3,  21,  11,  12,
        28,  76,   2,  14, 130,  19,  38,   6, 106,  14,   2,  13,  36,
         3,  21,  31,   4,   9,  91, 180,   1, 137,   1,   2,  87,  97,
        21,   5,   1, 285,  43,   1, 511, 569,  15, 775, 140,   1,   2,
        27,   7,  25,  68,  31, 184,  31,   2, 159, 174,  12,   1,   2,
        42,   1,   2,   9,   6,   1,   4,   1,  59,   8, 276, 258,   3,
       489,  37, 753, 544,  10,   4, 975, 313,  26,   2,  60,   6,   2,
        24,  15,   3,  16,  37, 112, 110,  32, 151,  70,   2,  24,  49,
        15,  47,  15,   3,  79,   8,  14, 191,  31,   2,  42, 105, 189,
       231,  15, 647,   2,  12,   8,   2,  19,  94, 118,  35,   1,   5,
        54,  19,   7, 141,   2,  27,  15,   1,  31,   2,  12, 347,  81,
        54,   7,  90,   8,   2, 410,   6,   2,  27,  15, 503,  62, 154,
        25, 143,   1,  15, 157, 134,   2, 174,  12,  17,  81, 390,   7,
         1,  16, 111,  15, 168,   2,  27,  15, 588, 329, 117,   7,   3,
       163,   5, 113, 947, 175,  26,   4, 643,   1,   2,  13,  30,  23,
        10,   1,   3,  21,  52,  12])>

Feed LSTM a sequence of one-hots

from keras.layers import CategoryEncoding, Bidirectional, LSTM
random.seed(42)
one_hot_model = Sequential([Input(shape=(max_length,), dtype="int64"),
    CategoryEncoding(num_tokens=max_tokens, output_mode="one_hot"),
    Bidirectional(LSTM(24)),
    Dense(1, activation="sigmoid")])
one_hot_model.compile(optimizer="adam",
    loss="binary_crossentropy", metrics=["accuracy"])
one_hot_model.summary()

Model: "sequential_4"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ category_encoding               │ (None, 500, 1000)      │             0 │
│ (CategoryEncoding)              │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ bidirectional (Bidirectional)   │ (None, 48)             │       196,800 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_8 (Dense)                 │ (None, 1)              │            49 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 196,849 (768.94 KB)

 Trainable params: 196,849 (768.94 KB)

 Non-trainable params: 0 (0.00 B)

Fit & evaluate

es = keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True,
    monitor="val_accuracy", verbose=2)

if not Path("one-hot-model.keras").exists():
    one_hot_model.fit(X_train_txt, y_train, epochs=1_000, callbacks=es,
        validation_data=(X_val_txt, y_val), verbose=0);
    one_hot_model.save("one-hot-model.keras")
else:
    one_hot_model = keras.models.load_model("one-hot-model.keras")

one_hot_model.evaluate(X_train_txt, y_train, verbose=0, batch_size=1_000)

[0.3188040852546692, 0.8918206095695496]

one_hot_model.evaluate(X_val_txt, y_val, verbose=0, batch_size=1_000)

[0.37093353271484375, 0.8776978254318237]

Custom embeddings

from keras.layers import Embedding
embed_lstm = Sequential([Input(shape=(max_length,), dtype="int64"),
    Embedding(input_dim=max_tokens, output_dim=32, mask_zero=True),
    Bidirectional(LSTM(24)),
    Dense(1, activation="sigmoid")])
embed_lstm.compile("adam", "binary_crossentropy", metrics=["accuracy"])
embed_lstm.summary()

Model: "sequential_5"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ embedding (Embedding)           │ (None, 500, 32)        │        32,000 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ bidirectional_1 (Bidirectional) │ (None, 48)             │        10,944 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_9 (Dense)                 │ (None, 1)              │            49 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 42,993 (167.94 KB)

 Trainable params: 42,993 (167.94 KB)

 Non-trainable params: 0 (0.00 B)

Fit & evaluate

es = keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True,
    monitor="val_accuracy", verbose=2)

if not Path("embed-lstm.keras").exists():
    embed_lstm.fit(X_train_txt, y_train, epochs=1_000, callbacks=es,
        validation_data=(X_val_txt, y_val), verbose=0);
    embed_lstm.save("embed-lstm.keras")
else:
    embed_lstm = keras.models.load_model("embed-lstm.keras")

embed_lstm.evaluate(X_train_txt, y_train, verbose=0, batch_size=1_000)

[0.27049171924591064, 0.9030942916870117]

embed_lstm.evaluate(X_val_txt, y_val, verbose=0, batch_size=1_000)

[0.36852043867111206, 0.8553956747055054]

embed_lstm.evaluate(X_test_txt, y_test, verbose=0, batch_size=1_000)

[0.3872850239276886, 0.8467625975608826]

Package Versions

from watermark import watermark
print(watermark(python=True, packages="keras,matplotlib,numpy,pandas,seaborn,scipy,torch,tensorflow,tf_keras"))

Python implementation: CPython
Python version       : 3.11.9
IPython version      : 8.24.0

keras     : 3.3.3
matplotlib: 3.9.0
numpy     : 1.26.4
pandas    : 2.2.2
seaborn   : 0.13.2
scipy     : 1.11.0
torch     : 2.3.1
tensorflow: 2.16.1
tf_keras  : 2.16.0

Glossary

• bag of words
• lemmatization
• n-grams
• one-hot embedding

• TF-IDF
• vocabulary
• word embedding
• word2vec