Week 8: Clustering and Text Mining Flashcards
1
Q
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- Stemming (running -> run)
- Lemmatisation (were -> is)
- lower casing
- Stop word removal
- Punctuation removal
- Number removal
- Spell correction
- Tokenisation
A
What are some typical steps in text pre-processing? (8)
2
Q
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the task of finding the document d from the D documents in some collection that best matches a query q
A
What is information retrieval?
3
Q
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Short, dense vectors that can be used to represent words?
A
What are embeddings?
4
Q
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- it is a token learner
- starts with vocabulary of all characters
- chooses the two symbols that are most frequently adjacent, adds a new merged symbol to the vocabulary and replaces every adjacent pair in the corpus with the new merged symbol
- continues to count and merge, creating new longer and longer character strings, until k merges have been done creating k novel tokens
A
How does the byte-pair encoding algorithm work for tokenisation?
5
Q
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it = itoken(wordvector) #create index tokens full\_vocab = create\_vocabulary(it) #create full vocabulary
A
How do you create a vocabulary of words in R?
6
Q
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train a classifier such that a given tuple (w,c) of a target word w paired with a candidate/context word c, it will return the probability that c is a real context word P(+|w,c)
A
What is the classifier to train for skip-grams?
7
Q
RevERSED
DocumentTermMatrix(corpus)
A
Code to create a document term matrix in R?
8
Q
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tidy()
A
Code to turn a document term matrix into a dataframe in R?
9
Q
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Similar rows mean that the words are similar because they occur in similar documents
A
When will two row vectors be similar in a term-document matrix?
10
Q
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Sentiment = total positive words - total negative words
A
What is the overall sentiment in sentiment analysis?
11
Q
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Each row is a document, each column is a word
A
What is a document-term matrix (DTM)?
12
Q
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Hidden groups within the data that are not recorded
A
What is gaussian mixture modelling trying to find?
13
Q
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latent class analysis latent profile analysis types of model based clustering
A
What do LCA and LPA stand for? what are they types of?
14
Q
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Corpus(textsource)
A
Code to create a corpus in R?
15
Q
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It separates out clitics (doesn’t becomes does n’t), keeps hyphenated words together, separates out all punctuatio
A
What does Penn Treebank tokenisation do?
16
Q
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- tokenising (segmenting) words
- normalising word formats
- segmenting sentences
A
What are 3 types of text normalisation?
17
Q
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create_tcm(it, vectorizer, skip_grams_window = 5)
A
How do you create a token co-occurrence matrix in R?
18
Q
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Use capture group to store the expression in memory
the (.*)er they were, the \1er they will be
A
How do you get part of a string and reference back to that part in an RE?
19
Q
RevERSED
adjust initial embeddings to maximise the similarity (dot product) of the (w, cpos) pairs drawn from the positive examples and minimise the similarity (dot product) of the (w, cneg) pairs from the negative examples
A
How does the skip-gram algorithm adjust during training?
20
Q
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words that occur in similar contexts tend to have similar meanings
A
What is the distributional hypothesis?
21
Q
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clustCombi(data=x)
A
What is the code for merging components of clusters in R?
22
Q
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two matrices W and C each containing an embedding for every one of the |V| words in the vocabulary V
A
What are all the parameters learned in skip-gram?
23
Q
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\n is newline
\t is tab
A
What are \n and \t in RE?
24
Q
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AFINN, NRC, bing
A
What are some popular lexicons for sentiment analysis? (3)
25
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complete morphological parsing of the word
ie. takes cats and parses it into the two morphemes cat and s
What is the most sophisticated way of lemmatisation?
26
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all prior class proportions 1/K, EII model, all posteriors are either 0 or 1
What type of Gaussian model mixture is k-means?
27
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```
tf = term frequency
idf = inverse document frequency
```
What are the terms of tf-idf
28
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```
p(x) = pi(1,x) Normal(mean1, var1) + (1-pi(1,x)) Normal(mean2, var2)
where pi(1,x) is the probability that variable x takes on value 1 (e.g. probability that person is man)
ie proportion of values expected in each cluster
```
What is the gaussian mixture model formula?
29
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the task of putting words/tokens in a standard format
What is word normalisation?
30
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represent a word as a point in multidimensional semantic space that is derived from the distributions of word neighbours
What are vector semantics for words?
31
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an algebraic notion for characterising a set of strings
What is a regular expression?
32
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P(+|w,c) = c.w = 1/1+exp(-c.w)
where . is the dot product
What is the probability that c is a context word P(+|w,c)?
33
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the frequency that a word appears is inversely proportional to its rank
What is Zipf's law?
34
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segmenting a text into sentences
What is sentence segmentation?
35
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same as data$variable
what does data %\>% pull(variable) do in R?
36
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The data within each cluster is normally distributed
What is the assumption of gaussian mixture modelling?
37
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n occurrences of the previous char or expression
from n to m occurrences of the previous char or expression
What do {n} and {m,n} mean in RE?
38
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AIC: Akaike information criterion - same as BIC but penalty is m
AIC3: same as AIC but penalty is 3m/2
ICL: Integrated information criterion - same as BIC but reconstruction loss includes the assigned clusters
What are 3 alternatives to the BIC?
39
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```
LCA = binomial mixture model
LPA = gaussian mixture model
```
What are other names for LCA and LPA
40
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cosine(v,w) = (v.w)/(|v||w|) (where . is dot product)
What is the formula for the cosine similarity measure?
41
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A clitic is a part of a word that can’t stand on its own, can only occur attached to another word. E.g. we’re
What is a clitic?
42
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Corpus = a collection of documents (our whole dataset)
Lexicon: set of all unique words in a corpus
What is a corpus and a lexicon?
43
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v.w = v1w1 + v2w2 + … + vNwN
How do you get the dot product of 2 vectors?
44
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Mclust(data, G=2, modelNames = “E”)
Code for implementing mclust in R?
45
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library(tidytext)
unnest\_tokens(data, outputcolumn, inputcolumn) #takes one term per row and automatically removes punctuation
How can you tokenise text in R?
46
# RevERSED
Columns and rows both represent words
- each cell records the number of times the row (target) word and the column (context) word co-occur in some context in some training corpus
- the context could be a document or smaller such as a window around the word
What is a term-term matrix or word co-occurrance matrix?
47
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bind\_tf\_idf(tidytext dataset, tokens, documents, counts) #takes a tidytext dataset as input with one row per token per document
How do you get the tf-idf in R?
48
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#replace matching expression with replacement
sub(“expression”, “replacement” data) (only first match of each index)
gsub(“expression”, “replacement” data) (all matches)
What do sub and gsub do?
49
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LCA lets the variables follow any distribution, as long as they are unrelated to each other (independent) within classes.
How does latent class analysis work?
50
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- Increasing precision: minimising false positives (strings that were incorrectly matched)
- Increasing recall: minimising false negatives (strings that were incorrectly missed)
What does reducing the error rate of an RE involve?
51
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\b matches word boundary
\B matches non-word boundary
What are \b and \B in RE?
52
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There are often deviations at high ranks, as a corpus often contains fewer rare words than predicted by a single power law
Where does Zipf's law often deviate?
53
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Tokenisation is the task of segmenting running text into words
What is tokenisation?
54
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\* zero or more occurrences of the immediately previous character or regular expression
+ one or more occurrences of the immediately previous character or regular expression
What is the kleene \* and knleene + in RE?
55
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grep(“expression”, data) #returns indices of all matching expressions from data
grep(“expression”, data, value = TRUE) #returns all the actual matching expressions from data. gives whole string, not just matching part
length(grep(“expression”, data)) #returns number of matches of expression in data
How do you use grep to match and count matches from data? (3 ways)
56
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- each row represents a word in the vocabulary
- each column represents a document
- each cell represents the number of times a particular word occurs in a particular document
What is a term-document matrix?
57
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"-" e.g. [2-5]
How do you specify a range in RE?
58
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1. Get some text
2. Organise text into corpus
3. Pre-process
4. Create representation
5. Perform analysis as usual
What is the basic workflow for text analysis? (5 steps)
59
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- A token learner takes a raw training corpus and induces a vocabulary, a set of tokens
- A token segmenter takes a raw test sentence and segments it into the tokens in the vocabulary
What is a token learner and a token segmenter?
60
# RevERSED
1. Treat the target word and a neighbouring context word as positive examples
2. Randomly sample other words in the lexicon to get negative samples
3. Use logistic regression to train a classifier to distinguish those two cases
4. Use the learned weights as the embeddings
What is the intuition of skip-gram/ word2vec?
61
# RevERSED
anything within the brackets can be included e.g. colo[ou]r means colour or color
What does [] mean in RE?
62
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r = regexpr(“expression”, data) #gives index of each match and length of each match (only for first match of each index)
r = gregexpr(“expression”, data) #gives index of each match and length of each match (all matches)
regmatches(data, r) #returns the actual matches obtained from regexpr or gregexpr
How do you use regexpr and gregexpr and regmatches to match data?
63
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- analogies: look at the analogies in vector space el.g. King - man + woman = queen
- Bias: semantics derived automatically from language corps contain human-like biases
What are two properties of word embeddings?
64
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1. Parenthesis ()
2. Counters \* + ? {}
3. Sequences and anchors ^ $
4. Disjunction |
What is the precedence hierarchy in RE?
65
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pi(k,X): number of classes -1 : K-1
mu(k): K\*p (p is number of features)
var: K\*p (or just p when variances are equal over classes)
covariances: K\*p\*(p-1)/2 (or p\*(p-1)/2 when covariances are equal over classes) (or 0 when variables are uncorrelated, spherical clusters)
m = (K-1) + Kp + Kp + Kp(p-1)/2
What are the number of parameters in a multivariate Gaussian mixture model:
66
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str\_view(“string", “expression”) #only matches the first expression
str\_detect(“string”, “expression”) #returns true/false depending on whether the string matches the expression
str\_extract(“string”, “expression”) #extracts the first match
str\_extract\_all(“string”, “expression”) #extracts all matches into a vector
str\_match\_all(“string”, “expression”) #similar to str\_extract\_all except output is matrix with column for each
What are the stringr options for matching strings?
67
# RevERSED
When the documents contain similar words
When will two column vectors be similar in a term-document matrix?
68
# RevERSED
pipe |
How can you say or in RE?
69
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Raw frequency is not the best measure of association between words because words like “the” and “good” occur frequently and aren’t informative. tf-idf gives weight to words that appear in fewer documents
What is the purpose of tf-idf?
70
# RevERSED
task of classifying the polarity of a given text (ie is it a good/bad review)
What is sentiment analysis?
71
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\\
How do you do an escape in R?
72
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all context words are independent
What assumption does skip-gram make?
73
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Self-supervised learning. Don't need labels
What is an advantage of word2vec/skip grams?
74
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matches the end of a line
What is $ in RE?
75
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a kind of normalisation that is mapping everything to lowercase
What is case folding?
76
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- Inflectional stemming: remove plurals, normalise verb tenses, remove other affixes
- Stemming to root: reduce word to most basic element
What is inflectional stemming and stemming to root?
77
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anti\_join(data, stop\_words) #removes stop words on an unnest\_tokens object
How can you remove stop words in R?
78
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How well the model fits the data
What does the likelihood tell us?
79
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start with the usual Gaussian mixture solution, merge similar components to create non-Gaussian clusters
How can you identify clusters that are not ellipses using the GMM?
80
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mclust fits all the models with up to specified number of clusters, computes the BIC of each model and chooses the model with the best BIC
How does mclust select a model in R?
81
# RevERSED
\d is any digit
\D is any non-digit
What are \d and \D in RE?
82
# RevERSED
naive version of morphological analysis that consists of chopping of word-final affixes
What is stemming?
83
# RevERSED
the task of determining that two words have the same root, despite their surface differences
What is lemmatisation?
84
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vectorizer = vocab\_vectorizer(small\_vocab)
How do you map words to indices in R? ie create a vectorizer
85
# RevERSED
term frequency: the frequency of the word t in document d / the total number of terms in the document
tf(t,d)= count(t,d)/total number of terms in the document
inverse document frequency: number of documents / number of documents the term occurs in
idf = log10(N/df(t))
tf-idf = tf\*idf
How do you calculate tf, idf and tf-idf?
86
# RevERSED
GloVe, fastText
What are other types of embeddings in R, after word2vec?
87
# RevERSED
Sparse because most are 0
What type of vector representations come from a term-term matrix?
88
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p(data|paramaters) = p(y|theta)
What is the likelihood, defined by the statistical model and the assumption?
89
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- Not great with longer tasks
- negation
- context-dependency
- You need partly labelled data
What are the problems with sentiment analysis? (4)
90
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the preceding character or nothing
e.g. colou?r
What is ? in RE?
91
# RevERSED
tradeoff between complexity and file size
the lower the better
What is the aim of the BIC?
92
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E for equal, V for variable, I for identity matrix
- Volume (size of clusters in data space)
- Shape (circle or ellipse)
- Orientation (the angle of the ellipse)
E.g. VVE model has variable volume, variable shape, equal orientation
What are the identifiers for each parameterisation of a GMM and what to they measure?
93
# RevERSED
- Bag-of-words: word count or word proportion for each word
- Time-series: label each token and put in order
- Tf-idf, embeddings
What are some forms of text representation? (4)
94
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The weighted sum of two normal curves
What is the overall probability curve made up of?
95
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Morphology is how words are build up from stems, the central morpheme of the word and affixes
What is morphology?
96
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sin2(matrix1, matrix2, method=“cosine”, norm=“l2”)
How do you calculate the cosine similarity in R?
97
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The cosine value ranges from 1 for vectors pointing in the same direction, through 0 for orthogonal vectors, to -1 for opposite vectors
What does the value of the cosine metric represent?
98
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pi(man,x) = point on male curve/point on total curve
How is the posterior probability calculated based on the current estimates of mean and sd?
99
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A wildcard expression that matches any single character
What is the period . in RE?
100
# RevERSED
0. Guess the parameters
1. Work out the posterior probability of being M/F assuming normality (E step)
2. Update the parameters (M step)
\* repeat steps 1 and 2 until parameters stop changing
What are the steps of the expectation maximisation (EM) algorithm?
101
# RevERSED
Caret ^ matches the start of a line
or negates the contents of [] if it is [^...]
What are the 2 things that ^ represents in RE
102
# RevERSED
Mean becomes a vector of 2 means
Standard deviation becomes a 2x2 variance covariance matrix determining the shape of the cluster
In multivariate model based clustering with 2 observed features, what do the mean and standard deviation become?
103
# RevERSED
fit\_mclust$paramaters #gives means, variances, proportions, modelName
fit\_mclust$bic #gives the bic for each option and shows which models have the lowest. These are negative in the mclust package, take absolute value
fit\_mclust$loglik #gives log likelihood used to calculate bic manually
fit\_mclust$classification #gives the cluster classification vector
fit\_mclust$uncertainty #gives uncertainty of each point
plot(fit\_mclust, “density”) #gives density plot
When you have fitted an Mclust model to fit\_mclust in R, what information can you obtain from the result?
104
# RevERSED
BIC = -2.log(l) + m.log(n)
```
l = likelihood = p(data|theta)
-2.log(l) = deviance = reconstruction loss = fit
m = number of parameters
n = number of observations/examples
m.log(n) = file size = complexity
```
What is the basis of information criteria (BIC) formula?
