## 5.6.2 Advanced manipulations

### 5.6.2.1 Tokens

`tokens()` segments texts in a corpus into tokens (words or sentences) by word boundaries. We can remove punctuations or not

```
dat social media corpus tok <- tokens (dat social media corpus)
dat social media corpus tok
```

#### 5.6.2.1.1 With punctuations

## Tokens consisting of 7,504 documents and 42 docvars.

### twitter.json.1 :

## [1] "@EFC_Jayy" "UKIP"

##

## twitter.json.2 :

## [1] "RT" "@Corbynator2" ":" "@jeremycorbyn"

## [5] "Reaction" "from" "people" "at"

## [9] "the" "Watford" "Rally" ":"

## [ ... and 18 more ]

##

## twitter.json.3 :

## [1] "RT" "@ryvr" ":" "Stephen" "Hawking" "," "world's" "smartest" "man" "," "backs"

## [ ... and 4 more ]

##

## twitter.json.4 :

## [1] "RT" "@TheGreenParty" ":" "How"

## [5] "you" "cast" "your" "vote"

## [9] "will" "shape" "the" "future"

## [ ... and 11 more ]

##

## twitter.json.5 :

## [1] "RT" "@UKLabour" ":" "#VoteLabour" "today"

## [6] "for" "a" "fairer" "Britain" "."

## [11] "Find" "out"

## [ ... and 11 more ]

##

## twitter.json.6 :

## [1] "RT" "@WestmonsterUK" ":" "POLLS"

## [5] ":" "FINAL" "DAY" "AVERAGE"

## [9] "CON" "43.8" "%" "LAB"

## [ ... and 10 more ]

##

## [ reached max_ndoc ... 7,498 more documents ]
```
dat social media corpus tok no punct <- tokens (dat social media corpus, remove punct = TRUE)
dat social media corpus tok no punct
```

### 5.6.2.1.2 Without punctuations

## Tokens consisting of 7,504 documents and 42 docvars.

```
## twitter.json.1 :
```

```
## [1] "@EFC_Jayy" "UKIP"
```

```R
##
```

```
## twitter.json.2 :
```

```
## [1] "RT" "@Corbynator2" "@jeremycorbyn" "Reaction"
## [5] "from" "people" "at" "the"
## [9] "Watford" "Rally" "We" "believe"
## [ ... and 10 more ]
```

```R
##
```

```
## twitter.json.3 :
```

```
## [1] "RT" "@ryvr"
## [3] "Stephen" "Hawking"
## [5] "the" "world's"
## [7] "smartest" "man"
## [9] "backs" "Jeremy"
## [11] "Corbyn" "https://t.co/2kl3ayLd44"
## [ ... and 1 more ]
```

```R
##
```

```R
##
```

```
## twitter.json.4 :
```

```
## [1] "RT" "@TheGreenParty" "How" "you"
## [5] "cast" "your" "vote" "will"
## [9] "shape" "the" "future" "Every"
## [ ... and 6 more ]
```

```R
##
```

```R
##
```

```
## twitter.json.5 :
```

```
## [1] "RT" "@UKLabour" "#VoteLabour" "today" "for"
## [6] "a" "fairer" "Britain" "Find" "out"
## [11] "where" "to"
## [ ... and 7 more ]
```

```R
##
```

```
## twitter.json.6 :
```

```
## [1] "RT" "@WestmonsterUK" "POLLS" "FINAL"
## [5] "DAY" "AVERAGE" "CON" "43.8"
## [9] "LAB" "35.4" "LD" "8"
## [ ... and 4 more ]
```

```R
##
```

```
## [ reached max_ndoc ... 7,498 more documents ]
```

### 5.6.2.2 Compound words
```
dat_twitter_corpus tok_no_punct phrase <- kwic (dat_twitter_corpus tok_no_punct, pattern = phrase
head (dat_twitter_corpus tok_no_punct phrase, 10)
```

### 5.6.2.2.1 kwic Phrase

```
## Keyword-in-context with 10 matches.
```

<table><tr><td>[twitter.json.112, 6:7]</td><td>RT@SocialistVoice People are mocking</td><td>the Tory</td></tr><tr><td>[twitter.json.131, 6:7]</td><td>RT@SocialistVoice People are mocking</td><td>the Tory</td></tr><tr><td>[twitter.json.155, 9:10]</td><td>Jeremy got an incredible welcome in</td><td>the Tory</td></tr><tr><td>[twitter.json.465, 6:7]</td><td>RT@SocialistVoice People are mocking</td><td>the Tory</td></tr><tr><td>[twitter.json.1824, 6:7]</td><td>RT@SocialistVoice People are mocking</td><td>the Tory</td></tr><tr><td>[twitter.json.1826, 6:7]</td><td>RT@SocialistVoice People are mocking</td><td>the Tory</td></tr><tr><td>[twitter.json.1927, 6:7]</td><td>RT@SocialistVoice People are mocking</td><td>the Tory</td></tr><tr><td>[twitter.json.2301, 6:7]</td><td>RT@SocialistVoice People are mocking</td><td>the Tory</td></tr><tr><td>[twitter.json.2380, 6:7]</td><td>RT@SocialistVoice People are mocking</td><td>the Tory</td></tr><tr><td>[twitter.json.2514, 6:7]</td><td>RT@SocialistVoice People are mocking</td><td>the Tory</td></tr><tr><td colspan="3">##</td></tr><tr><td colspan="3">## gutter press with hashtag#LastMinuteCorbynSmears#VoteLabour</td></tr><tr><td colspan="3">## gutter press with hashtag#LastMinuteCorbynSmears#VoteLabour</td></tr><tr><td colspan="3">## stronghold of Watford a little while</td></tr><tr><td colspan="3">## gutter press with hashtag#LastMinuteCorbynSmears#VoteLabour</td></tr><tr><td colspan="3">## gutter press with hashtag#LastMinuteCorbynSmears#VoteLabour</td></tr><tr><td colspan="3">## gutter press with hashtag#LastMinuteCorbynSmears#VoteLabour</td></tr><tr><td colspan="3">## gutter press with hashtag#LastMinuteCorbynSmears#VoteLabour</td></tr><tr><td colspan="3">## gutter press with hashtag#LastMinuteCorbynSmears#VoteLabour</td></tr><tr><td colspan="3">## gutter press with hashtag#LastMinuteCorbynSmears#VoteLabour</td></tr></table>

```
dat_twitter_corpus tok_no_punct_comp <- tokens_compound (dat_twitter_corpus tok_no_punct, pattern = phrase
 Dat-Twitter-Compound- kwic <- kwic (dat_twitter_corpus tok_no_punct_comp, pattern = phrase
head (dat_twitter_corpus tok_no_punct_comp kwic, 10)
```

### 5.6.2.2.2 Compounds

```
## Keyword-in-context with 10 matches.
```

<table><tr><td>[twitter.json.112, 6]</td><td>RT@SocialistVoice People are mocking</td><td>the_Tory</td></tr><tr><td>[twitter.json.131, 6]</td><td>RT@SocialistVoice People are mocking</td><td>the_Tory</td></tr><tr><td>[twitter.json.155, 9]</td><td>got an incredible welcome in</td><td>the_Tory</td></tr><tr><td>[twitter.json.465, 6]</td><td>RT@SocialistVoice People are mocking</td><td>the_Tory</td></tr><tr><td>[twitter.json.1824, 6]</td><td>RT@SocialistVoice People are mocking</td><td>the_Tory</td></tr><tr><td>[twitter.json.1826, 6]</td><td>RT@SocialistVoice People are mocking</td><td>the_Tory</td></tr><tr><td>[twitter.json.1927, 6]</td><td>RT@SocialistVoice People are mocking</td><td>the_Tory</td></tr><tr><td>[twitter.json.2301, 6]</td><td>RT@SocialistVoice People are mocking</td><td>the_Tory</td></tr><tr><td>[twitter.json.2380, 6]</td><td>RT@SocialistVoice People are mocking</td><td>the_Tory</td></tr><tr><td>[twitter.json.2514, 6]</td><td>RT@SocialistVoice People are mocking</td><td>the_Tory</td></tr></table>
##
## gutter press with hashtag#LastMinuteCorbynSmears
## gutter press with hashtag#LastMinuteCorbynSmears
## stronghold of Watford a little
## gutter press with hashtag#LastMinuteCorbynSmears
## gutter press with hashtag#LastMinuteCorbynSmears
## gutter press with hashtag#LastMinuteCorbynSmears
## gutter press with hashtag#LastMinuteCorbynSmears
## gutter press with hashtag#LastMinuteCorbynSmears
## gutter press with hashtag#LastMinuteCorbynSmears
## gutter press with hashtag#LastMinuteCorbynSmears

5.6.2.3 N-grams

N-grams are a subfamily of compound words. They can be named as “bi-grams”,
“tri-grams”, etc. N-grams yield a sequence of tokens from already tokenised text
object.

5.6.2.3.1 Multi-grams The code below allows to obtain the sequences of
consecutive compound words, with 2, 3 or 4 compound words.

datالفیاراکاوسکوونکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوسA

## .
##                 -                 - - 8
##                 10                - - #ge2017
##                 - - -             - - #ge2017_https://t.co/x5k8hydpka
##                 6                 2
##                 _ _#ge2017 _ _#ge2017_https://t.co/x5k8hydpka
##                 2                 2
##                 _#ge2017 _ #ge2017_https://t.co/x5k8hydpka
##                 2                 2
##                 _do                _do_not
##                 1                 1

## Last 10 rows
tail (datالفیاراکاوسکوونکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوساکاوسA

## .
##  - - -         -   - -   - - -
##   1          1          1          1
##   -          - - -          -
<table><tr><td>##</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>##</td><td>--</td><td>--</td><td>1</td><td>1</td></tr></table>

5.6.2.3.2 Skip-grams Skip-grams allow to obtain non consecutive n-grams

```
dat.twitter_corpus tok no punct ngram_skip <- tokens_ngrams (dat.twitter_corpus tok no punct, n = unlist() %>% tolower() %>% table ()
## Top 10 rows
head (dat.twitter_corpus tok no punct ngram_skip, 10)
```

<table><tr><td>##</td><td></td><td></td><td></td></tr><tr><td>##</td><td></td><td>14</td><td>8</td></tr><tr><td>##</td><td></td><td>__ #ge2017</td><td>__ https://t.co/x5k8hydpka</td></tr><tr><td>##</td><td></td><td>2</td><td>8</td></tr><tr><td>##</td><td></td><td>__ #ge2017</td><td>__ https://t.co/x5k8hydpka</td></tr><tr><td>##</td><td></td><td>8</td><td>8</td></tr><tr><td>##</td><td></td><td>__ #ge2017</td><td>__ https://t.co/x5k8hydpka</td></tr><tr><td>##</td><td></td><td>4</td><td>4</td></tr><tr><td>##</td><td></td><td>_not</td><td>_not_pencil</td></tr><tr><td>##</td><td></td><td>1</td><td>1</td></tr></table>

```
## Last 10 rows
tail (dat.twitter_corpus tok no punct ngram_skip, 10)
```

<table><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>##</td><td></td><td>1</td><td></td><td>1</td><td></td><td>1</td><td>1</td></tr><tr><td>##</td><td></td><td>1</td><td></td><td>1</td><td></td><td>1</td><td>1</td></tr></table>

5.6.2.4 Dictionary

If you have a dictionary with various words that fall within a generic word (e.g., variants of pronunciation of a word), then you can look these up. Here, we will create a dictionary that we populate ourselves and we show how to use it to search for items

```
dict_dat_twitter <- dictionary (list (Tories = c ("Tory*", "Conservative*"), Labour = c ("Socialist*", "Labour*")))
print (dict_dat_twitter)
```

5.6.2.4.1 Create dictionary
## Dictionary object with 2 key entries.
## - [Tories]:
## - tory*, conservative*
## - [Labour]:
## - socialist*, labour*

```
dat_twitter_corpus_tokens <- tokens_lookup.dat_twitter_corpustok_no_punct_dict_tokens
print.dat_twitter_corpustok_no_punct_dict_tokens)
```

### 5.6.2.4.2 Token lookup

## Tokens consisting of 7,504 documents and 42 docvars.
## twitter.json.1 :
## character(0)
##
## twitter.json.2 :
## character(0)
##
## twitter.json.3 :
## character(0)
##
## twitter.json.4 :
## character(0)
##
## twitter.json.5 :
## character(0)
##
## twitter.json.6 :
## character(0)
##
## [ reached max_ndoc ... 7,498 more documents ]

```
dfm.dat_twitter_corpustok_no_punct_dict_tokens)
```

### 5.6.2.4.3 DFM

## Document-feature matrix of: 7,504 documents, 2 features (85.65% sparse) and 42 docvars
## features
## docs          tories labour
## twitter.json.1  0          0
## twitter.json.2  0          0
## twitter.json.3  0          0
## twitter.json.4  0          0
## twitter.json.5  0          0
## twitter.json.6  0          0
## [ reached max_ndoc ... 7,498 more documents ]

### 5.6.2.5 Part of Speech tagging

Part-of-Speech tagging (or PoS-Tagging) is used to distinguish different part of speech, e.g., the sentence: "Jane likes the girl" can be tagged as "Jane/NNP likes/VBZ the/DT girl/NN", where NNP = proper noun (singular), VBZ = 3rd person singular present tense verb, DT = determiner, and NN = noun (singular or mass). We will use the udpipe package

#### 5.6.2.5.1 Download and load language model
Before using the PoS-tagger, we need to download a language model. As you can see from typing `?udpipe_download_model`, there are 65 languages trained on 101 treebanks from here

```
file_to_check <- "models/english-ewt-ud-2.5-191206.udpipe"
```

```
if (file.exists(file = file_to_check)){
  m_english <- udpipe_load_model(file = "models/english-ewt-ud-2.5-191206.udpipe")
}else{
```

```
  m_english <- udpipe_download_model(model_dir = "models/", language = "english-ewt")
  m_english <- udpipe_load_model(file = "models/english-ewt-ud-2.5-191206.udpipe")
}
```

#### 5.6.2.5.2 Tokenise, tag, dependency parsing
We use the already tokenised text, with no punctuations.

```
dat_twitter_anndf <- udpipe_ annotate(m_english, x = dat_twitter_corpus_token_no_punct[[2]]) %>
```

```
## inspect
```

```
head(dat_twitter_anndf, 10)
```

<table><thead><tr><th>doc_id</th><th>paragraph_id</th><th>sentence_id</th><th>sentence</th><th>token_id</th><th>token</th></tr></thead><tbody><tr><td>1</td><td>doc1</td><td>1</td><td>1</td><td>RT</td><td>RT</td></tr><tr><td>2</td><td>doc2</td><td>1</td><td>1</td><td>@Corbynator2</td><td>1</td><td>@Corbynator2</td></tr><tr><td>3</td><td>doc3</td><td>1</td><td>1</td><td>@jeremycorbyn</td><td>1</td><td>@jeremycorbyn</td></tr><tr><td>4</td><td>doc4</td><td>1</td><td>1</td><td>Reaction</td><td>1</td><td>Reaction</td></tr><tr><td>5</td><td>doc5</td><td>1</td><td>1</td><td>from</td><td>1</td><td>from</td></tr><tr><td>6</td><td>doc6</td><td>1</td><td>1</td><td>people</td><td>1</td><td>people</td></tr><tr><td>7</td><td>doc7</td><td>1</td><td>1</td><td>at</td><td>1</td><td>at</td></tr><tr><td>8</td><td>doc8</td><td>1</td><td>1</td><td>the</td><td>1</td><td>the</td></tr><tr><td>9</td><td>doc9</td><td>1</td><td>1</td><td>Watford</td><td>1</td><td>Watford</td></tr><tr><td>10</td><td>doc10</td><td>1</td><td>1</td><td>Rally</td><td>1</td><td>Rally</td></tr><tr><td></td><td>lemma</td><td>upos</td><td>xpos</td><td></td><td>feats</td><td>head_token_id dep_rel</td></tr><tr><td>1</td><td></td><td>Rt</td><td>PROPN</td><td>NNP</td><td>Number=Sing</td><td>0 root</td></tr><tr><td>2</td><td></td><td></td><td>X</td><td>ADD</td><td>&lt;NA&gt;</td><td>0 root</td></tr><tr><td>3</td><td></td><td></td><td>NOUN</td><td>NN</td><td>Number=Sing</td><td>0 root</td></tr></tbody></table>
<table><tr><td>## 4</td><td>reaction</td><td>NOUN</td><td>NN</td><td>Number=Sing</td><td>0</td><td>root</td></tr><tr><td>## 5</td><td>from</td><td>ADP</td><td>IN</td><td>&lt;NA&gt;</td><td>0</td><td>root</td></tr><tr><td>## 6</td><td>people</td><td>NOUN</td><td>NNS</td><td>Number=Plur</td><td>0</td><td>root</td></tr><tr><td>## 7</td><td>at</td><td>ADP</td><td>IN</td><td>&lt;NA&gt;</td><td>0</td><td>root</td></tr><tr><td>## 8</td><td>the</td><td>DET</td><td>DT</td><td>Definite=Def|PronType=Art</td><td>0</td><td>root</td></tr><tr><td>## 9</td><td>Watford</td><td>PROPN</td><td>NNP</td><td>Number=Sing</td><td>0</td><td>root</td></tr><tr><td>## 10</td><td>rally</td><td>ADV</td><td>RB</td><td>&lt;NA&gt;</td><td>0</td><td>root</td></tr><tr><td>##</td><td>deps</td><td></td><td>misc</td><td></td><td></td><td></td></tr><tr><td>## 1</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td><td></td></tr><tr><td>## 2</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td><td></td></tr><tr><td>## 3</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td><td></td></tr><tr><td>## 4</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td><td></td></tr><tr><td>## 5</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td><td></td></tr><tr><td>## 6</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td><td></td></tr><tr><td>## 7</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td><td></td></tr><tr><td>## 8</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td><td></td></tr><tr><td>## 9</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td><td></td></tr><tr><td>## 10</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td><td></td></tr></table>

### parse text

```
dat_twitter_corpus_sent <- udpipe_ annotate(m_english, x = dat_twitter_corpus[[2]]) %>%
as.data.frame()
```

### inspect

```
head.dat_twitter_corpussent)
```

#### 5.6.2.5.3 Dependency parsing

<table><thead><tr><th>doc_id</th><th>paragraph_id</th><th>sentence_id</th><th></th><th></th></tr></thead><tbody><tr><td>1</td><td>doc1</td><td>1</td><td>1</td><td></td></tr><tr><td>2</td><td>doc1</td><td>1</td><td>1</td><td></td></tr><tr><td>3</td><td>doc1</td><td>1</td><td>2</td><td></td></tr><tr><td>4</td><td>doc1</td><td>1</td><td>2</td><td></td></tr><tr><td>5</td><td>doc1</td><td>1</td><td>2</td><td></td></tr><tr><td>6</td><td>doc1</td><td>1</td><td>2</td><td></td></tr></tbody></table><table><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>1</td><td></td><td></td><td></td><td>RT @Corbynator2:@jeremycorbyn</td><td></td></tr><tr><td>2</td><td></td><td></td><td></td><td>RT @Corbynator2:@jeremycorbyn</td><td></td></tr><tr><td>3</td><td>Reaction from people at the Watford Rally: "We believe in Jeremy Corbyn!"</td><td></td><td></td><td></td><td></td></tr><tr><td>4</td><td>Reaction from people at the Watford Rally: "We believe in Jeremy Corbyn!"</td><td></td><td></td><td></td><td></td></tr><tr><td>5</td><td>Reaction from people at the Watford Rally: "We believe in Jeremy Corbyn!"</td><td></td><td></td><td></td><td></td></tr><tr><td>6</td><td>Reaction from people at the Watford Rally: "We believe in Jeremy Corbyn!"</td><td></td><td></td><td></td><td></td></tr><tr><td>token_id</td><td></td><td>token</td><td></td><td>lemma</td><td>upos</td></tr><tr><td>1</td><td></td><td>RT</td><td></td><td>Rt</td><td>PROPN</td></tr><tr><td>2</td><td></td><td>2 @Corbynator2:@jeremycorbyn</td><td></td><td>@Corbynator2:@jeremycorbyn</td><td>PROPN</td></tr><tr><td>3</td><td></td><td>Reaction</td><td></td><td>reaction</td><td>NOUN</td></tr><tr><td>4</td><td></td><td></td><td></td><td></td><td>NN</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>## 4</td><td>2</td><td>from</td><td>from</td><td>ADP</td><td>IN</td><td></td></tr><tr><td>## 5</td><td>3</td><td>people</td><td>people</td><td>NOUN</td><td>NNS</td><td></td></tr><tr><td>## 6</td><td>4</td><td>at</td><td>at</td><td>ADP</td><td>IN</td><td></td></tr><tr><td colspan="7">## feats head_token_id dep_rel deps misc</td></tr><tr><td>## 1</td><td>Number=Sing</td><td>0</td><td>root</td><td>&lt;NA&gt;</td><td>&lt;NA&gt;</td><td></td></tr><tr><td>## 2</td><td>Number=Sing</td><td>1</td><td>flat</td><td>&lt;NA&gt;</td><td>&lt;NA&gt;</td><td></td></tr><tr><td>## 3</td><td>Number=Sing</td><td>0</td><td>root</td><td>&lt;NA&gt;</td><td>&lt;NA&gt;</td><td></td></tr><tr><td>## 4</td><td>&lt;NA&gt;</td><td>3</td><td>case</td><td>&lt;NA&gt;</td><td>&lt;NA&gt;</td><td></td></tr><tr><td>## 5</td><td>Number=Plur</td><td>1</td><td>nmod</td><td>&lt;NA&gt;</td><td>&lt;NA&gt;</td><td></td></tr><tr><td>## 6</td><td>&lt;NA&gt;</td><td>7</td><td>case</td><td>&lt;NA&gt;</td><td>&lt;NA&gt;</td><td></td></tr></tbody></table>

```
dat_twitter_corpus_sent[3:21,]
```

```
dat_twitter_corpus_sent_dplot <- textplot_dependencycparser (dat_twitter_corpus_sent_short, size =
## show plot
```

```
dat_twitter_corpus_sent_dplot
```

## Dependency Parser

tokenisation, parts of speech tagging & dependency relations

<figure><img src="image_153.png" /></figure>

### 5.6.2.6 Feature co-occurrence matrix (FCM)

Feature co-occurrence matrix (FCM) records the number of co-occurrences of tokens

```
dat_twitter_corpus DFmat <- dfm (dat_twitter_corpus tok_no_punct)
```

```
dat_twitter_corpus DFmat Trim <- dfm Trim (dat_twitter_corpus DFmat, min_termfreq = 50)
```

```
topfeatures dat_twitter_corpus <- topfeatures (dat_twitter_corpus DFmat Trim)
```

```
topfeatures dat_twitter_corpus
```
### 5.6.2.6.1 Computing number of co-occurrences

rt the to a #ge2017 for vote and of in
5827 3973 3418 2627 2483 2334 1886 1738 1738 1598

nfeat (datالفاتcorpus_dfmat\_trim)

[1] 383

dat\_twitter\_corpus\_fcm < fcm (dat\_twitter\_corpus\_dfmat\_trim)

dat\_twitter\_corpus\_fcm

### 5.6.2.6.2 Features co-occurrences

Feature co-occurrence matrix of: 383 by 383 features.

<table><thead><tr><th></th><th></th><th> features</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td> features</td><td> ukip</td><td> rt</td><td> @corbynator2</td><td> @jeremycorbyn</td><td> from</td><td> people</td><td> at</td><td> the</td><td> rally</td></tr><tr><td> ukip</td><td> 4</td><td> 194</td><td> 0</td><td> 0</td><td> 6</td><td> 2</td><td> 8</td><td> 86</td><td> 0</td></tr><tr><td> rt</td><td> 0</td><td> 236</td><td> 171</td><td> 434</td><td> 292</td><td> 326</td><td> 207</td><td> 3165</td><td> 95</td></tr><tr><td> @corbynator2</td><td> 0</td><td> 0</td><td> 0</td><td> 168</td><td> 4</td><td> 7</td><td> 23</td><td> 91</td><td> 23</td></tr><tr><td> @jeremycorbyn</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 11</td><td> 42</td><td> 36</td><td> 377</td><td> 23</td></tr><tr><td> from</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 3</td><td> 10</td><td> 15</td><td> 269</td><td> 27</td></tr><tr><td> people</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 16</td><td> 24</td><td> 276</td><td> 5</td></tr><tr><td> at</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 17</td><td> 187</td><td> 41</td></tr><tr><td> the</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 1311</td><td> 104</td></tr><tr><td> rally</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 10</td></tr><tr><td> we</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td><td> 0</td></tr></tbody></table>

features

<table><tr><td> features</td><td> we</td></tr><tr><td> ukip</td><td> 9</td></tr><tr><td> rt</td><td> 451</td></tr><tr><td> @corbynator2</td><td> 11</td></tr><tr><td> @jeremycorbyn</td><td> 26</td></tr><tr><td> from</td><td> 26</td></tr><tr><td> people</td><td> 37</td></tr><tr><td> at</td><td> 25</td></tr><tr><td> the</td><td> 268</td></tr><tr><td> rally</td><td> 9</td></tr><tr><td> we</td><td> 139</td></tr></table>

[ reached max_nfeat ... 373 more features, reached max_nfeat ... 373 more features ]
## 5.7 Single web page

### 5.7.1 Read_html

```python
web_page <- rvest::read_html("https://www.tidyverse.org/packages/")
web_page
```

```r
## {html_document}
## <html>
## [1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" ...
## [2] <body>\n<div id="appTidyverseSite" class="shrinkHeader alwaysShrinkHeader ...
```

Because the downloaded file contains an unnecessary information. We process the data to extract only the text from the webpage.

### 5.7.2 Extract headline

```r
header_web_page <- web_page %>%
  ## extract paragraphs
  rvest::html_nodes("h1") %>%
  ## extract text
  rvest::html_text()

head(header_web_page)
```

```r
## [1] "Tidyverse packages"
```

### 5.7.3 Extract text

```r
web_page_txt <- web_page %>%
  ## extract paragraphs
  rvest::html_nodes("p") %>%
  ## extract text
  rvest::html_text()

head(web_page_txt)
```

```r
## [1] "Install all the packages in the tidyverse by running install/packages("tidyverse")."
## [2] "Run library(tidyverse) to load the core tidyverse and make it available\nin your current
## [3] "Learn more about the tidyverse package at https://tidyverse.tidyverse.org.""
## [4] "The core tidyverse includes the packages that you're likely to use in everyday data analysis"
## [5] "ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics"
## [6] "dplyr provides a grammar of data manipulation, providing a consistent set of verbs that can be composed"
```

### 5.7.4 Create a corpus

```r
web_page_txt_corpus <- corpus(web_page_txt)
print(web_page_txt_corpus)
```
## Corpus consisting of 31 documents.
## text1 :
## "Install all the packages in the tidyverse by running install..."
##
## text2 :
## "Run library(tidyverse) to load the core tidyverse and make i..."
##
## text3 :
## "Learn more about the tidyverse package at https://tidyverse...."
##
## text4 :
## "The core tidyverse includes the packages that you're likely ..."
##
## text5 :
## "ggplot2 is a system for declaratively creating graphics, bas..."
##
## text6 :
## "dplyr provides a grammar of data manipulation, providing a c..."
##
## [ reached max_ndoc ... 25 more documents ]

5.7.4.1 Summary

summary(web_page_txt_corpus, 10)

## Corpus consisting of 31 documents, showing 10 documents:
##
##   Text Types Tokens Sentences
## text1   13    16        1
## text2   19    20        1
## text3    9     9        1
## text4   24    31        2
## text5   37    50        3
## text6   22    29        2
## text7   30    46        3
## text8   40    51        3
## text9   45    57        3
## text10  48    64        3

5.7.4.2 Accessing parts of corpus

web_page_txt_corpus[[4]]

## [1] "The core tidyverse includes the packages that you're likely to use in everyday
### 5.7.4.3 Document-level information

```
head(docvars(web_page_txt_corp)
```

```python
## data frame with 0 columns and 6 rows
```

## 5.7.5 Advanced manipulations

### 5.7.5.1 Tokens

`tokens()` segments texts in a corpus into tokens (words or sentences) by word boundaries. We can remove punctuations or not

```
web_page_txt_corp_token <- tokens(web_page_txt_corp)
web_page_txt_corp_token
```

#### 5.7.5.1.1 With punctuations

```r
## Tokens consisting of 31 documents.
```

```r
## text1 :
```

```r
## [1] "Install"      "all"          "the"          "packages"
## [5] "in"            "the"          "tidyverse"    "by"
## [9] "running"        "install.packages" "("             "\"
## [ ... and 4 more ]
```

```r
##
```

```r
## text2 :
```

```r
## [1] "Run"          "library"      "("             "tidyverse" ")"      "to"
## [7] "load"         "the"          "core"          "tidyverse"    "and"      "make"
## [ ... and 8 more ]
```

```r
##
```

```r
## text3 :
```

```r
## [1] "Learn"          "more"
## [3] "about"          "the"
## [5] "tidyverse"      "package"
## [7] "at"             "https://tidyverse.tidyverse.org"
## [9] "."
```

```r
##
```

```r
## text4 :
```

```r
## [1] "The"          "core"          "tidyverse"    "includes"    "the"          "packages"
## [7] "that"         "you're"         "likely"        "to"           "use"          "in"
## [ ... and 19 more ]
```

```r
##
```

```r
## text5 :
```

```r
## [1] "ggplot2"      "is"            "a"             "system"
## [5] "for"           "declaratively"  "creating"      "graphics"
## [9] ","           "based"          "on"             "The"
```
## [ ... and 38 more ]
##
## text6 :
## [1] "dplyr" "provides" "a" "grammar" "of"
## [6] "data" "manipulation" "," "providing" "a"
## [11] "consistent" "set"
## [ ... and 17 more ]
##
## [ reached max_ndoc ... 25 more documents ]

web_page_txt_corpus_tokens <- tokens(web_page_txt_corpus, remove_punct = TRUE)
web_page_txt_corpus_tokens

5.7.5.1.2 Without punctuations

## Tokens consisting of 31 documents.
## text1 :
## [1] "Install" "all" "the" "packages"
## [5] "in" "the" "tidyverse" "by"
## [9] "running" "install.packages" "tidyverse"
##
## text2 :
## [1] "Run" "library" "tidyverse" "to" "load" "the"
## [7] "core" "tidyverse" "and" "make" "it" "available"
## [ ... and 5 more ]
##
## text3 :
## [1] "Learn" "more"
## [3] "about" "the"
## [5] "tidyverse" "package"
## [7] "at" "https://tidyverse.tidyverse.org"
##
## text4 :
## [1] "The" "core" "tidyverse" "includes" "the" "packages"
## [7] "that" "you're" "likely" "to" "use" "in"
## [ ... and 16 more ]
##
## text5 :
## [1] "ggplot2" "is" "a" "system"
## [5] "for" "declaratively" "creating" "graphics"
## [9] "based" "on" "The" "Grammar"
## [ ... and 29 more ]
##
## text6 :
## [1] "dplyr" "provides" "a" "grammar" "of"
## [6] "data" "manipulation" "providing" "a" "consistent"
## [11] "set" "of"
## [ ... and 12 more ]
##
## [ reached max_ndoc ... 25 more documents ]

### 5.7.5.2 Compound words

web_page_txt_corpus_tokens_no_punct_phrase <- kwic(web_page_txt_corpus_tokens_no_punct, pattern = phrase, head(web_page_txt_corpus_tokens_no_punct_phrase, 10))

#### 5.7.5.2.1 kwic Phrase

## Keyword-in-context with 6 matches.
## [text1, 6:7] Install all the packages in | the tidyverse |
## [text3, 4:5] Learn more about | the tidyverse |
## [text14, 1:2] | The tidyverse |
## [text15, 9:10] as readr for reading flat files | the tidyverse |
## [text28, 8:9] provides the pipe > used throughout | the tidyverse |
## [text30, 3:4] Modeling with | the tidyverse |
##
## by running install/packages tidyverse
## package at https://tidyverse.tidyverse.org
## also includes many other packages with
## package installs a number of other
## It also provide a number of
## uses the collection of tidymodels packages

web_page_txt_corpus_tokens_no_punct_comp <- tokens_compound(web_page_txt_corpus_tokens_no_punct, pattern = phrase, head(web_page_txt_corpus_tokens_no_punct_comp, 10))

#### 5.7.5.2.2 Compounds

## Keyword-in-context with 6 matches.
## [text1, 6] Install all the packages in | the tidyverse |
## [text3, 4] Learn more about | the tidyverse |
## [text14, 1] | The tidyverse |
## [text15, 9] readr for reading flat files | the tidyverse |
## [text28, 8] the pipe > used throughout | the tidyverse |
## [text30, 3] Modeling with | the tidyverse |
##
## by running install/packages tidyverse
## package at https://tidyverse.tidyverse.org
## also includes many other packages
## package installs a number of
## It also provide a number
## uses the collection of tidymodels

### 5.7.5.3 N-grams

N-grams are a subfamily of compound words. They can be named as “bi-grams”, “tri-grams”, etc. N-grams yield a sequence of tokens from already tokenised text object.

#### 5.7.5.3.1 Multi-grams
The code below allows to obtain the sequences of consecutive compound words, with 2, 3 or 4 compound words.

```r
web_page_txt_corpus_tokens <- tokens_ngrams(web_page_txt_corpus_tokens,
                                              unlist() %>%
                                              tolower() %>%
                                              table())

## Top 10 rows
head(web_page_txt_corpus_tokens, 10)
```

```r
## .
##       $_and      $_and_<      $_and_<_      <_>       <_>_that
##           1          1          1          1          1
##  <_>_that_can  >_that      >_that_can  >_that_can_be  >_used
##           1          1          1          1          1
```

```
## Last 10 rows
tail(web_page_txt_corpus_tokens, 10)
```

```r
## .
##  you're_asking_for_r      you're_likely      you're_likely_to
##                          1                  1                  1
##  you're_likely_to_use      you're_more        you're_more_likely
##                          1                  1                  1
##  you're_more_likely_to     your_current       your_current_r
##                          1                  1                  1
## your_current_r_session
##                          1
```

#### 5.7.5.3.2 Skip-grams
Skip-grams allow to obtain non consecutive n-grams

```r
web_page_txt_corpus_tokens_skip <- tokens_ngrams(web_page_txt_corpus_tokens,
                                                  unlist() %>%
                                                  tolower() %>%
                                                  table())

## Top 10 rows
head(web_page_txt_corpus_tokens_skip[[1]], 10)
```

```r
## [1] "Install_the"      "Install-packages" "all-packages"
## [4] "all_in"           "the_in"           "the_the"
## [7] "packages_the"     "packages_tidyverse" "in_tidyverse"
```
```python
## [10] "in_by"
## Last 10 rows

tail(web_page_txt_corpus_tokens_no_punct_ngram_skip[[1]], 10)
```

```python
## [1] "the_in_by_install.packages"
## [2] "the_in_by_tidyverse"
## [3] "the_the_by_install.packages"
## [4] "the_the_by_tidyverse"
## [5] "the_the_running_tidyverse"
## [6] "packages_the_by_install.packages"
## [7] "packages_the_by_tidyverse"
## [8] "packages_the_running_tidyverse"
## [9] "packages_tidyverse_running_tidyverse"
## [10] "in_tidyverse_running_tidyverse"
```

### 5.7.5.4 Dictionary

If you have a dictionary with various words that fall within a generic word (e.g., variants of pronunciation of a word), then you can look these up. Here, we will create a dictionary that we populate ourselves and we show how to use it to search for items

```python
dict_web_page_txt <- dictionary(list(tidy_family = c("tidy*", "ggplot**"),
                                   r-packages = "*r"))
print(dict_web_page_txt)
```

#### 5.7.5.4.1 Create dictionary

```r
## Dictionary object with 2 key entries.
## - [tidy_family]:
##   - tidy*, ggplot**
## - [r-packages]:
##   - *r
```

```r
web_page_txt_corpus_tokens_no_punct_dict_tokens <- tokens_lookup(web_page_txt_corpus_tokens_no_punct, dic)
print(web_page_txt_corpus_tokens_no_punct_dict_tokens)
```

#### 5.7.5.4.2 Token lookup

```r
## Tokens consisting of 31 documents.
## text1 :
## [1] "tidy_family" "tidy_family"
##
## text2 :
## [1] "tidy_family" "tidy_family" "r-packages" "r-packages"
```
##
## text3 :
## [1] "tidy_family"
##
## text4 :
## [1] "tidy_family" "tidy_family" "tidy_family"
##
## text5 :
## [1] "tidy_family" "r-packages" "r-packages" "tidy_family"
##
## text6 :
## [1] "r-packages" "r-packages"
##
## [ reached max_ndoc ... 25 more documents ]

dfm(web_page_txt_corpus tok_no_punct_dict_toks)

5.7.5.4.3 DFM

## Document-feature matrix of: 31 documents, 2 features (35.48% sparse) and 0 docvars.
## features
docs tidy_family r-packages
## text1 2 0
## text2 2 2
## text3 1 0
## text4 3 0
## text5 2 2
## text6 0 2
## [ reached max_ndoc ... 25 more documents ]

5.7.5.5 Part of Speech tagging

Part-of-Speech tagging (or PoS-Tagging) is used to distinguish different part of
speech, e.g., the sentence: “Jane likes the girl” can be tagged as “Jane/NNP
likes/VBZ the/DT girl/NN”, where NNP = proper noun (singular), VBZ = 3rd
person singular present tense verb, DT = determiner, and NN = noun (singular
or mass). We will use the udpipe package

5.7.5.5.1 Download and load language model Before using the PoS-
tagger, we need to download a language model. As you can see from typing
?udpipe_download_model, there are 65 languages trained on 101 treebanks
from here

file_to_check <- "models/english-ewt-ud-2.5-191206.udpipe"

if (file.exists(file = file_to_check)){
```
m_english <- udpipe_load_model(file = "models/english-ewt-ud-2.5-191206.udpipe")
}else{
m_english <- udpipe_download_model(model_dir = "models/", language = "english-ewt")
m_english <- udpipe_load_model(file = "models/english-ewt-ud-2.5-191206.udpipe")
}
```

### 5.7.5.5.2 Tokenise, tag, dependency parsing
We use the already tokenised text, with no punctuations.

```
web_page_txt_anndf <- udpipe_ annotate(m_english, x = web_page_txt_corpus_token_no_punct[[1]]) %>%
as.data.frame()
## inspect
head(web_page_txt_anndf, 10)
```

<table><thead><tr><th></th><th>doc_id</th><th>paragraph_id</th><th>sentence_id</th><th>sentence_token_id</th><th>token</th></tr></thead><tbody><tr><td>## 1</td><td>doc1</td><td>1</td><td>1</td><td>Install</td><td>1 Install</td></tr><tr><td>## 2</td><td>doc2</td><td>1</td><td>1</td><td>all</td><td>1 all</td></tr><tr><td>## 3</td><td>doc3</td><td>1</td><td>1</td><td>the</td><td>1 the</td></tr><tr><td>## 4</td><td>doc4</td><td>1</td><td>1</td><td>packages</td><td>1 packages</td></tr><tr><td>## 5</td><td>doc5</td><td>1</td><td>1</td><td>in</td><td>1 in</td></tr><tr><td>## 6</td><td>doc6</td><td>1</td><td>1</td><td>the</td><td>1 the</td></tr><tr><td>## 7</td><td>doc7</td><td>1</td><td>1</td><td>tidyverse</td><td>1 tidyverse</td></tr><tr><td>## 8</td><td>doc8</td><td>1</td><td>1</td><td>by</td><td>1 by</td></tr><tr><td>## 9</td><td>doc9</td><td>1</td><td>1</td><td>running</td><td>1 running</td></tr><tr><td>## 10</td><td>doc10</td><td>1</td><td>1</td><td>install/packages</td><td>1 install/packages</td></tr><tr><td>##</td><td>lemma</td><td>upos</td><td>xpos</td><td>feats</td><td>head_token_id dep_rel</td></tr><tr><td>## 1</td><td>install</td><td>ADV</td><td>RB</td><td>&lt;NA&gt;</td><td>0 root</td></tr><tr><td>## 2</td><td>all</td><td>DET</td><td>DT</td><td>&lt;NA&gt;</td><td>0 root</td></tr><tr><td>## 3</td><td>the</td><td>DET</td><td>DT Definite=Def|PronType=Art</td><td></td><td>0 root</td></tr><tr><td>## 4</td><td>packages</td><td>NOUN</td><td>NNS</td><td>Number=Plur</td><td>0 root</td></tr><tr><td>## 5</td><td>in</td><td>ADP</td><td>IN</td><td>&lt;NA&gt;</td><td>0 root</td></tr><tr><td>## 6</td><td>the</td><td>DET</td><td>DT Definite=Def|PronType=Art</td><td></td><td>0 root</td></tr><tr><td>## 7</td><td>tidyverse</td><td>NOUN</td><td>NN</td><td>Number=Sing</td><td>0 root</td></tr><tr><td>## 8</td><td>by</td><td>ADP</td><td>IN</td><td>&lt;NA&gt;</td><td>0 root</td></tr><tr><td>## 9</td><td>run</td><td>VERB</td><td>VBG</td><td>VerbForm=Ger</td><td>0 root</td></tr><tr><td>## 10</td><td>install/package</td><td>NOUN</td><td>NNS</td><td>Number=Plur</td><td>0 root</td></tr><tr><td>##</td><td>deps</td><td></td><td>misc</td><td></td><td></td></tr><tr><td>## 1</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td></tr><tr><td>## 2</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td></tr><tr><td>## 3</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td></tr><tr><td>## 4</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td></tr><tr><td>## 5</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td></tr><tr><td>## 6</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td></tr><tr><td>## 7</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td></tr><tr><td>## 8</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td><td></td><td></td><td></td></tr></tbody></table>
## 9 <NA> SpacesAfter=\\n

## 10 <NA> SpacesAfter=\\n

## parse text

web_page_txt_corpus_sent <- udpipe_ annotate(m_english, x = web_page_txt_corpus[[1]]) %>% as.data.frame()

## inspect

head(web_page_txt_corpus_sent)

### 5.7.5.5.3 Dependency parsing

## doc_id paragraph_id sentence_id

<table><tr><td>## 1</td><td>doc1</td><td>1</td><td>1</td></tr><tr><td>## 2</td><td>doc1</td><td>1</td><td>1</td></tr><tr><td>## 3</td><td>doc1</td><td>1</td><td>1</td></tr><tr><td>## 4</td><td>doc1</td><td>1</td><td>1</td></tr><tr><td>## 5</td><td>doc1</td><td>1</td><td>1</td></tr><tr><td>## 6</td><td>doc1</td><td>1</td><td>1</td></tr></table>

## senten
text
## 1 Install all the packages in the tidyverse by running install/packages("tidyverse")
## 2 Install all the packages in the tidyverse by running install/packages("tidyverse")
## 3 Install all the packages in the tidyverse by running install/packages("tidyverse")
## 4 Install all the packages in the tidyverse by running install/packages("tidyverse")
## 5 Install all the packages in the tidyverse by running install/packages("tidyverse")
## 6 Install all the packages in the tidyverse by running install/packages("tidyverse")
## token_id token lemma upos xpos feats head_token_id
## 1 1 Install install ADV RB <NA> 4
## 2 2 all all DET PDT <NA> 4
## 3 3 the the DET DT Definite=Def|PronType=Art 4
## 4 4 packages packages NOUN NNS Number=Plur 0
## 5 5 in in ADP IN <NA> 7
## 6 6 the the DET DT Definite=Def|PronType=Art 7
## dep_rel deps misc
## 1 advmod <NA> <NA>
## 2 det:predet <NA> <NA>
## 3 det <NA> <NA>
## 4 root <NA> <NA>
## 5 case <NA> <NA>
## 6 det <NA> <NA>

web_page_txt_corpus_sent_dplot <- textplot_dependencyparser(web_page_txt_corpus_sent, ...

## show plot

web_page_txt_corpus_sent_dplot
Dependency Parser

tokenisation, parts of speech tagging & dependency relations

<figure><img src="image_154.png" /></figure>

5.7.5.6 Feature co-occurrence matrix (FCM)

Feature co-occurrence matrix (FCM) records the number of co-occurrences of
tokens

web_page_txt_corpus DFmat <- dfm(web_page_txt_corpus tok_no_punct)
web_page_txt_corpus DFmat_trim <- dfm_trim(web_page_txt_corpus dfmat, min_termfreq = 5)
topfeatures_web_page_txt_corpus <- topfeatures(web_page_txt_corpus dfmat_trim)
topfeatures_web_page_txt_corpus

5.7.5.6.1 Computing number of co-occurrences

<table>
  <tr>
    <td>##</td>
    <td>to</td>
    <td>the</td>
    <td>and</td>
    <td>a</td>
    <td>for</td>
    <td>of</td>
    <td>tidyverse</td>
    <td>data</td>
  </tr>
  <tr>
    <td>##</td>
    <td>37</td>
    <td>30</td>
    <td>21</td>
    <td>21</td>
    <td>21</td>
    <td>20</td>
    <td>15</td>
    <td>15</td>
  </tr>
  <tr>
    <td>##</td>
    <td>with</td>
    <td>in</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>##</td>
    <td>15</td>
    <td>13</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
</table>

nfeat(web_page_txt_corpus DFmat_trim)

## [1] 25

web_page_txt_corpus fcmat <- fcm(web_page_txt_corpus dfmat_trim)
web_page_txt_corpus fcmat

5.7.5.6.2 Features co-occurrences
## Feature co-occurrence matrix of: 25 by 25 features.
## features
## features the packages in tidyverse to and it more that data
## the 22 24 23 30 45 21 9 16 10 15
## packages 0 2 10 15 10 5 0 3 4 4
## in 0 0 3 15 18 12 3 4 6 13
## tidyverse 0 0 0 6 13 7 3 5 5 5
## to 0 0 0 0 27 45 17 19 19 30
## and 0 0 0 0 0 8 9 11 12 17
## it 0 0 0 0 0 0 0 3 3 6
## more 0 0 0 0 0 0 0 2 4 2
## that 0 0 0 0 0 0 0 0 1 8
## data 0 0 0 0 0 0 0 0 0 7
## [ reached max_nfeat ... 15 more features, reached max_nfeat ... 15 more features ]

## 5.8 Multiple webpages

### 5.8.1 Read html

```r
website <- "https://www.tidyverse.org/packages/" %>%
  rvest::read_html()
website
```

```r
## {html_document}
## <html>
## [1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" ...
## [2] <body>\n<div id="appTidyverseSite" class="shrinkHeader alwaysShrinkHeader ...

a_elements <- website %>%
  rvest::html_elements.css = "div/package > a")
a_elements
```

```r
## {xml_nodeset (9)}
## [1] <a href="https://ggplot2.tidyverse.org/" target="_blank"><img class="pack" ...
## [2] <a href="https://dplyr.tidyverse.org/" target="_blank"><img class="packag" ...
## [3] <a href="https://tidyr.tidyverse.org/" target="_blank"><img class="packag" ...
## [4] <a href="https://readr.tidyverse.org/" target="_blank"><img class="packag" ...
## [5] <a href="https://purrr.tidyverse.org/" target="_blank"><img class="packag" ...
## [6] <a href="https://tibble.tidyverse.org/" target="_blank"><img class="packa" ...
## [7] <a href="https://stringr.tidyverse.org/" target="_blank"><img class="pack" ...
## [8] <a href="https://forcats.tidyverse.org/" target="_blank"><img class="pack" ...
## [9] <a href="https://lubridate.tidyverse.org/" target="_blank"><img class="pa" ...
```
## 5.8.2 Extract headline

```
links <- a_elements %>%
  rvest::html_attr(name = "href")
links
```

```
## [1] "https://ggplot2.tidyverse.org/" "https://dplyr.tidyverse.org/"
## [2] "https://tidyr.tidyverse.org/" "https://readr.tidyverse.org/"
## [3] "https://purrr.tidyverse.org/" "https://tibble.tidyverse.org/"
## [4] "https://stringr.tidyverse.org/" "https://forcats.tidyverse.org/"
## [5] "https://lubridate.tidyverse.org/"
```

## 5.8.3 Extract subpages

```
pages <- links %>%
  map(rvest::read_html)
pages
```

```
## [[1]]
## {html_document}
## <html lang="en">
## [1] <head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...</head>
## [2] <body><a href="#container" class="visually-hidden-focusable">Skip t ...</a></body>
##
## [[2]]
## {html_document}
## <html lang="en">
## [1] <head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...</meta></head>
## [2] <body><a href="#container" class="visually-hidden-focusable">Skip t ...</a></body>
##
## [[3]]
## {html_document}
## <html lang="en">
## [1] <head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...</meta></head>
## [2] <body><a href="#container" class="visually-hidden-focusable">Skip t ...</a></body>
##
## [[4]]
## {html_document}
## <html lang="en">
## [1] <head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...</meta></head>
## [2] <body><a href="#container" class="visually-hidden-focusable">Skip t ...</a></body>
##
## [[5]]
## {html_document}
## <html lang="en">
## [1] <head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...</meta></head>
```
## [2] <body>
<a href="#container" class="visually-hidden-focusable">Skip t ...
##
## [[6]]
## {html_document}
## <html lang="en">
## [1] <head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...
## [2] <body>
<a href="#container" class="visually-hidden-focusable">Skip t ...
##
## [[7]]
## {html_document}
## <html lang="en">
## [1] <head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...
## [2] <body>
<a href="#container" class="visually-hidden-focusable">Skip t ...
##
## [[8]]
## {html_document}
## <html lang="en">
## [1] <head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...
## [2] <body>
<a href="#container" class="visually-hidden-focusable">Skip t ...
##
## [[9]]
## {html_document}
## <html lang="en">
## [1] <head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...
## [2] <body>
<a href="#container" class="visually-hidden-focusable">Skip t ...
##

The structure seems to be similar across all pages

pages %>
map(rvest::html_element, css = "a.navbar-brand") %>
map CHR(rvest::html_text)

## [1] "ggplot2" "dplyr" "tidyr" "readr" "purrr" "tibble"
## [7] "stringr" "forcats" "lubridate"

and extracting version number

pages %>
map(rvest::html_element, css = "small.nav-text.text-muted.me-auto") %>
map CHR(rvest::html_text)

## [1] "4.0.1" "1.1.4" "1.3.1" "2.1.6" "1.2.0" "3.3.0" "1.6.0" "1.0.1" "1.9.4"

and we can also add all into a tibble

5.8.4 Extract text
```python
pages_table <- tibble(
  name = pages %>
  map(rvest::html_element, css = "a.navbar-brand") %>
  map chr(rvest::html_text),
  version = pages %>
  map(rvest::htmlElement, css = "small.nav-text.text-muted.me-auto") %>
  map chr(rvest::html_text),
  CRAN = pages %>
  map(rvest::htmlElement, css = "ul.list-unstyled > li:nth-child(1) > a") %>
  map chr(rvest::html_attr, name = "href"),
  Learn = pages %>
  map(rvest::htmlElement, css = "ul.list-unstyled > li:nth-child(4) > a") %>
  map chr(rvest::html_attr, name = "href"),
  text = pages %>
  map(rvest::htmlElement, css = "body") %>
  map chr(rvest::html_text2)
)
pages_table
```

```r
## # A tibble: 9 x 5
## name      version CRAN                 Learn   text
## <chr>     <chr>    <chr>                 <chr>    <chr>
## 1 ggplot2  4.0.1    https://cloud.r-project.org/package=ggplot2  https:// ~ "Ski"
## 2 dplyr    1.1.4    https://cloud.r-project.org/package=dplyr         http:// ~ "Ski"
## 3 tidyr    1.3.1    https://cloud.r-project.org/package=tidyr         https:// ~ "Ski"
## 4 readr    2.1.6    https://cloud.r-project.org/package=readr         http:// ~ "Ski"
## 5 purrr    1.2.0    https://cloud.r-project.org/package=purrr         http:// ~ "Ski"
## 6 tibble  3.3.0    https://cloud.r-project.org/package=tibble         https:// ~ "Ski"
## 7 stringr  1.6.0    https://cloud.r-project.org/package=stringr         http:// ~ "Ski"
## 8 forcats  1.0.1    https://cloud.r-project.org/package=forcats         http:// ~ "Ski"
## 9 lubridate 1.9.4  https://cloud.r-project.org/package=lubridate       https:// ~ "Ski"
```

### 5.8.5 Create a corpus

```r
web_pages_txt_corpus <- corpus(pages_table)
print(web_pages_txt_corpus)

## Corpus consisting of 9 documents and 4 docvars.
## text1 :
## "Skip to content ggplot24.0.1 Get started Reference News Rele..."
##
## text2 :
## "Skip to content dplyr1.1.4 Get started Reference Articles Gr..."
##
## text3 :
```
## "Skip to content tidyr1.3.1 Tidy data Reference Articles Pivo..."
##
## text4 :
## "Skip to content readr2.1.6 Get started Reference Articles Co..."
##
## text5 :
## "Skip to content purrr1.2.0 Get started Reference Articles pu..."
##
## text6 :
## "Skip to content tibble3.3.0 Get started Reference Articles C..."
##
## [ reached max_ndoc ... 3 more documents ]

5.8.5.1 Summary

summary(web_pages_txt_corpus, 10)

## Corpus consisting of 9 documents, showing 9 documents:

<table><thead><tr><th></th><th>Text</th><th>Types</th><th>Tokens</th><th>Sentences</th><th></th><th>name</th><th>version</th></tr></thead><tbody><tr><td>##</td><td>text1</td><td>377</td><td>797</td><td>25</td><td>ggplot2</td><td>4.0.1</td><td></td></tr><tr><td>##</td><td>text2</td><td>419</td><td>1258</td><td>17</td><td>dplyr</td><td>1.1.4</td><td></td></tr><tr><td>##</td><td>text3</td><td>326</td><td>729</td><td>25</td><td>tidyr</td><td>1.3.1</td><td></td></tr><tr><td>##</td><td>text4</td><td>569</td><td>1751</td><td>47</td><td>readr</td><td>2.1.6</td><td></td></tr><tr><td>##</td><td>text5</td><td>251</td><td>499</td><td>11</td><td>purrr</td><td>1.2.0</td><td></td></tr><tr><td>##</td><td>text6</td><td>269</td><td>717</td><td>14</td><td>tibble</td><td>3.3.0</td><td></td></tr><tr><td>##</td><td>text7</td><td>401</td><td>1355</td><td>23</td><td>stringr</td><td>1.6.0</td><td></td></tr><tr><td>##</td><td>text8</td><td>264</td><td>645</td><td>14</td><td>forcats</td><td>1.0.1</td><td></td></tr><tr><td>##</td><td>text9</td><td>267</td><td>650</td><td>11</td><td>lubridate</td><td>1.9.4</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>CRAN</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://cloud.r-project.org/package=ggplot2</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://cloud.r-project.org/package=dplyr</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://cloud.r-project.org/package=tidyr</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://cloud.r-project.org/package=readr</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://cloud.r-project.org/package=purrr</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://cloud.r-project.org/package=tibble</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://cloud.r-project.org/package=stringr</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://cloud.r-project.org/package=forcats</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://cloud.r-project.org/package=lubridate</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td></td><td>Learn</td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://r4ds.had.co.nz/data-visualisation.html</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>http://r4ds.had.co.nz/transform.html</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://r4ds.hadley.nz/data-tidy</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>http://r4ds.had.co.nz/data-import.html</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>http://r4ds.had.co.nz/iteration.html</td><td></td></tr><tr><td>##</td><td></td><td></td><td></td><td></td><td></td><td>https://r4ds.had.co.nz/tibbles.html</td><td></td></tr></tbody></table>
## http://r4ds.hadley.nz/strings.html

## http://r4ds.had.co.nz/factors.html

## https://r4ds.hadley.nz/datetimes.html

### 5.8.5.2 Accessing parts of corpus

web_pages_txt_corpus[[4]]

[1] "Skip to content\nreadr2.1.6\nGet started\nReference\nArticles\nColumn type\nLocales\nNews the guessed column types, in this case. This is useful because it allows you to check that the co

### 5.8.5.3 Document-level information

head(docvars(web_pages_txt_corpus))

<table><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>##</td><td>name</td><td>version</td><td>CRAN</td></tr><tr><td>##</td><td>1</td><td>ggplot2</td><td>4.0.1 https://cloud.r-project.org/package=ggplot2</td></tr><tr><td>##</td><td>2</td><td>dplyr</td><td>1.1.4 https://cloud.r-project.org/package=dplyr</td></tr><tr><td>##</td><td>3</td><td>tidyr</td><td>1.3.1 https://cloud.r-project.org/package=tidyr</td></tr><tr><td>##</td><td>4</td><td>readr</td><td>2.1.6 https://cloud.r-project.org/package=readr</td></tr><tr><td>##</td><td>5</td><td>purrr</td><td>1.2.0 https://cloud.r-project.org/package=purrr</td></tr><tr><td>##</td><td>6</td><td>tibble</td><td>3.3.0 https://cloud.r-project.org/package=tibble</td></tr><tr><td>##</td><td></td><td></td><td>Learn</td></tr><tr><td>##</td><td>1</td><td>https://r4ds.had.co.nz/data-visualisation.html</td><td></td></tr><tr><td>##</td><td>2</td><td>http://r4ds.had.co.nz/transform.html</td><td></td></tr><tr><td>##</td><td>3</td><td>https://r4ds.hadley.nz/data-tidy</td><td></td></tr><tr><td>##</td><td>4</td><td>http://r4ds.had.co.nz/data-import.html</td><td></td></tr><tr><td>##</td><td>5</td><td>http://r4ds.had.co.nz/iteration.html</td><td></td></tr><tr><td>##</td><td>6</td><td>https://r4ds.had.co.nz/tibbles.html</td><td></td></tr></tbody></table>

## 5.8.6 Advanced manipulations

### 5.8.6.1 Tokens

tokens() segments texts in a corpus into tokens (words or sentences) by word boundaries. We can remove punctuations or not

web_pages_txt_corpus_tokens <- tokens(web_pages_txt_corpus)
web_pages_txt_corpus_tokens

#### 5.8.6.1.1 With punctuations

## Tokens consisting of 9 documents and 4 docvars.

## text1 :

## [1] "Skip" "to" "content" "ggplot24.0.1" "Get"
## [6] "started" "Reference" "News" "Releases" "Version"
## [11] "4.0.0" "Version"
## [ ... and 785 more ]
##
## text2 :
## [1] "Skip" "to" "content" "dplyr1.1.4" "Get"
## [6] "started" "Reference" "Articles" "Grouped" "data"
## [11] "Two-table" "verbs"
## [ ... and 1,246 more ]
##
## text3 :
## [1] "Skip" "to" "content" "tidyr1.3.1" "Tidy"
## [6] "data" "Reference" "Articles" "Pivoting" "Rectangling"
## [11] "Nested" "data"
## [ ... and 717 more ]
##
## text4 :
## [1] "Skip" "to" "content" "readr2.1.6" "Get"
## [6] "started" "Reference" "Articles" "Column" "type"
## [11] "Locales" "News"
## [ ... and 1,739 more ]
##
## text5 :
## [1] "Skip" "to" "content" "purrr1.2.0" "Get"
## [6] "started" "Reference" "Articles" "purrr" "<"
## [11] "-" ">"
## [ ... and 487 more ]
##
## text6 :
## [1] "Skip" "to" "content" "tibble3.3.0" "Get"
## [6] "started" "Reference" "Articles" "Column" "types"
## [11] "Controlling" "display"
## [ ... and 705 more ]
##
## [ reached max_ndoc ... 3 more documents ]

web_pages_txt_corpus_tokens <- tokens(web_pages_txt_corpus, remove_punct = TRUE)
web_pages_txt_corpus_tokens

### 5.8.6.1.2 Without punctuations

## Tokens consisting of 9 documents and 4 docvars.

## text1 :

## [1] "Skip" "to" "content" "ggplot24.0.1" "Get"
## [6] "started" "Reference" "News" "Releases" "Version"
## [11] "4.0.0" "Version"
## [ ... and 652 more ]
##
## text2 :
## [1] "Skip" "to" "content" "dplyr1.1.4" "Get"
## [6] "started" "Reference" "Articles" "Grouped" "data"
## [11] "Two-table" "verbs"
## [ ... and 988 more ]
##
## text3 :
## [1] "Skip" "to" "content" "tidyr1.3.1" "Tidy"
## [6] "data" "Reference" "Articles" "Pivoting" "Rectangling"
## [11] "Nested" "data"
## [ ... and 547 more ]
##
## text4 :
## [1] "Skip" "to" "content" "readr2.1.6" "Get"
## [6] "started" "Reference" "Articles" "Column" "type"
## [11] "Locales" "News"
## [ ... and 1,370 more ]
##
## text5 :
## [1] "Skip" "to" "content" "purrr1.2.0" "Get"
## [6] "started" "Reference" "Articles" "purrr" "<"
## [11] ">" "base"
## [ ... and 380 more ]
##
## text6 :
## [1] "Skip" "to" "content" "tibble3.3.0" "Get"
## [6] "started" "Reference" "Articles" "Column" "types"
## [11] "Controlling" "display"
## [ ... and 536 more ]
##
## [ reached max_ndoc ... 3 more documents ]

### 5.8.6.2 Compound words

web_pages_txt_corpus tok_no_punct_phrase <- kwic(web_pages_txt_corpus tok_no_punct, pattern = ph

#### 5.8.6.2.1 kwic Phrase

## Keyword-in-context with 4 matches.
## [text1, 350:351] give you a comprehensive introduction to
## [text1, 504:505] announcements and deep-dives you can visit
## [text3, 109:110] that is used wherever possible throughout
## [text3, 477:478] please use community.rstudio.com Please note that
##
## the tidyverse | and these two chapters will get
## the tidyverse | blog Getting help There are two
## the tidyverse | If you ensure that your data
## the tidyverse | project is released with a Contributor

web_pages_txt_corpus_tokens_no_punct_comp <- tokens_compound(web_pages_txt_corpus_tokens_no_punct_comp)
web_pages_txt_corpus_tokens_no_punct_comp_kwic <- kwic(web_pages_txt_corpus_tokens_no_punct_comp_kwic)
head(web_pages_txt_corpus_tokens_no_punct_comp_kwic, 10)

### 5.8.6.2.2 Compounds

## Keyword-in-context with 4 matches.

## [text1, 350] you a comprehensive introduction to | the_tidyverse |
## [text1, 503] and deep-dives you can visit | the_tidyverse |
## [text3, 109] is used wherever possible throughout | the_tidyverse |
## [text3, 476] use community.rstudio.com Please note that | the_tidyverse |
##
## and these two chapters will
## blog Getting help There are
## If you ensure that your
## project is released with a

### 5.8.6.3 N-grams

N-grams are a subfamily of compound words. They can be named as “bi-grams”, “tri-grams”, etc. N-grams yield a sequence of tokens from already tokenised text object.

#### 5.8.6.3.1 Multi-grams
The code below allows to obtain the sequences of consecutive compound words, with 2, 3 or 4 compound words.

web_pages_txt_corpus_tokens_no_punct_ngram <- tokens_ngrams(web_pages_txt_corpus_tokens_no_punct_ngram)
unlist() %>%
toower() %>%
table()
## Top 10 rows
head(web_pages_txt_corpus_tokens_no_punct_ngram, 10)

## .
##       $_cyl      $_cyl_ | $_cyl_>_      ^_2
##           1          1          1          2
##       ^_2_+      ^_2_+_y   ^_2_>      ^_2>_select
##           1          1          1          1
## `_show_col_types` `_show_col_types_=
##           1          1
```
## Last 10 rows

tail(web_pages_txt_corpus tok_no_punct_ngram, 10)
```

<table><thead><tr><th>zero_length Outputs</th><th>learn</th><th>zones_leap</th></tr></thead><tbody><tr><td>1</td><td></td><td>1</td></tr><tr><td>zones_leap_days</td><td></td><td>zones_leap_days_daylight</td></tr><tr><td>1</td><td></td><td>1</td></tr><tr><td>zones_with_tz</td><td></td><td>zones_with_tz_force_tz</td></tr><tr><td>1</td><td></td><td>1</td></tr><tr><td>zones_with_tz_force_tz_time</td><td></td><td>_license</td></tr><tr><td>1</td><td></td><td>1</td></tr><tr><td>_license_full</td><td></td><td>_license_full_license</td></tr><tr><td>1</td><td></td><td>1</td></tr></tbody></table>

### 5.8.6.3.2 Skip-grams
Skip-grams allow to obtain non consecutive n-grams

```
web_pages_txt_corpus tok_no_punct_ngram_skip <- tokens_ngrams(web_pages_txt_corpus tok_no_punct,
## Top 10 rows

head(web_pages_txt_corpus tok_no_punct_ngram_skip[[7]], 10)
```

<table><tr><td>[1] "Skip_content"</td><td>"Skip_stringr1.6.0"</td><td>"to_stringr1.6.0"</td></tr><tr><td>[4] "to_Get"</td><td>"content_Get"</td><td>"content_started"</td></tr><tr><td>[7] "stringr1.6.0_started"</td><td>"stringr1.6.0_Reference"</td><td>"Get_Reference"</td></tr><tr><td>[10] "Get_Articles"</td><td></td><td></td></tr></table>

```
## Last 10 rows

tail(web_pages_txt_corpus tok_no_punct_ngram_skip[[7]], 10)
```

<table><tr><td>[1] "Developed_Hadley_built_pkgdown"</td><td>"Developed_Hadley_built_2.1.3"</td></tr><tr><td>[3] "Developed_Wickham_built_pkgdown"</td><td>"Developed_Wickham_built_2.1.3"</td></tr><tr><td>[5] "Developed_Wickham_with_2.1.3"</td><td>"by_Wickham_built_pkgdown"</td></tr><tr><td>[7] "by_Wickham_built_2.1.3"</td><td>"by_Wickham_with_2.1.3"</td></tr><tr><td>[9] "by_Site_with_2.1.3"</td><td>"Hadley_Site_with_2.1.3"</td></tr></table>

### 5.8.6.4 Dictionary

If you have a dictionary with various words that fall within a generic word (e.g., variants of pronunciation of a word), then you can look these up. Here, we will create a dictionary that we populate ourselves and we show how to use it to search for items

```
dict_web_pages_txt <- dictionary(list(tidy_family = c("tidy*", "ggplot**"),
                                   rorum = "*r"))

print(dict_web_pages_txt)
```
```
## [6] "r-packages" "r-packages" "r-packages" "r-packages" "r-packages"
## [11] "tidy_family" "tidy_family"
## [ ... and 16 more ]
##
## [ reached max_ndoc ... 3 more documents ]
```

```
dfm(web_pages_txt_corpus_tokens_no_punct_dict_tokens)
```

### 5.8.6.4.3 DFM

```
## Document-feature matrix of: 9 documents, 2 features (0.00% sparse) and 4 docvars.
##       features
## docs   tidy_family r-packages
##   text1      34      42
##   text2        3      105
##   text3       29       51
##   text4       10      112
##   text5        3       40
##   text6        3       25
## [ reached max_ndoc ... 3 more documents ]
```

### 5.8.6.5 Part of Speech tagging

Part-of-Speech tagging (or PoS-Tagging) is used to distinguish different part of speech, e.g., the sentence: "Jane likes the girl" can be tagged as "Jane/NNP likes/VBZ the/DT girl/NN", where NNP = proper noun (singular), VBZ = 3rd person singular present tense verb, DT = determiner, and NN = noun (singular or mass). We will use the udpipe package

#### 5.8.6.5.1 Download and load language model
Before using the PoS-tagger, we need to download a language model. As you can see from typing `?udpipe_download_model`, there are 65 languages trained on 101 treebanks from here

```
file_to_check <- "models/english-ewt-ud-2.5-191206.udpipe"

if (file.exists(file = file_to_check)){
  m_english <- udpipe_load_model(file = "models/english-ewt-ud-2.5-191206.udpipe")
}else{
  m_english <- udpipe_download_model(model_dir = "models/", language = "english-ewt")
  m_english <- udpipe_load_model(file = "models/english-ewt-ud-2.5-191206.udpipe")
}
```

#### 5.8.6.5.2 Tokenise, tag, dependency parsing
We use the already tokenised text, with no punctuations.
```
web_pages_txt_annndf <- udpipe_ annotate(m_english, x = web_pages_txt_corpus_token_no_punct_as_data.frame())
```

```
## inspect
```

```
head(web_pages_txt_annndf, 10)
```

<table><thead><tr><th>doc_id</th><th>paragraph_id</th><th>sentence_id</th><th>sentence</th><th>token_id</th><th>token</th></tr></thead><tbody><tr><td>1</td><td>doc1</td><td>1</td><td>Skip</td><td>1</td><td>Skip</td></tr><tr><td>2</td><td>doc2</td><td>1</td><td>to</td><td>1</td><td>to</td></tr><tr><td>3</td><td>doc3</td><td>1</td><td>content</td><td>1</td><td>content</td></tr><tr><td>4</td><td>doc4</td><td>1</td><td>ggplot24.0.1</td><td>1</td><td>ggplot24.0.1</td></tr><tr><td>5</td><td>doc5</td><td>1</td><td>Get</td><td>1</td><td>Get</td></tr><tr><td>6</td><td>doc6</td><td>1</td><td>started</td><td>1</td><td>started</td></tr><tr><td>7</td><td>doc7</td><td>1</td><td>Reference</td><td>1</td><td>Reference</td></tr><tr><td>8</td><td>doc8</td><td>1</td><td>News</td><td>1</td><td>News</td></tr><tr><td>9</td><td>doc9</td><td>1</td><td>Releases</td><td>1</td><td>Releases</td></tr><tr><td>10</td><td>doc10</td><td>1</td><td>Version</td><td>1</td><td>Version</td></tr><tr><td></td><td>lemma</td><td>upos</td><td>xpos</td><td>feats</td><td>head_token_id</td></tr><tr><td>1</td><td>skip</td><td>NOUN</td><td>NN</td><td>Number=Sing</td><td>0</td></tr><tr><td>2</td><td>to</td><td>ADP</td><td>IN</td><td>&lt;NA&gt;</td><td>0</td></tr><tr><td>3</td><td>content</td><td>NOUN</td><td>NN</td><td>Number=Sing</td><td>0</td></tr><tr><td>4</td><td>ggplot24.0.1</td><td>NOUN</td><td>NN</td><td>Number=Sing</td><td>0</td></tr><tr><td>5</td><td>get</td><td>VERB</td><td>VB</td><td>Mood=Imp|VerbForm=Fin</td><td>0</td></tr><tr><td>6</td><td>start</td><td>VERB</td><td>VBD</td><td>Mood=Ind|Tense=Past|VerbForm=Fin</td><td>0</td></tr><tr><td>7</td><td>reference</td><td>NOUN</td><td>NN</td><td>Number=Sing</td><td>0</td></tr><tr><td>8</td><td>news</td><td>NOUN</td><td>NN</td><td>Number=Sing</td><td>0</td></tr><tr><td>9</td><td>releases</td><td>NOUN</td><td>NNS</td><td>Number=Plur</td><td>0</td></tr><tr><td>10</td><td>version</td><td>NOUN</td><td>NN</td><td>Number=Sing</td><td>0</td></tr><tr><td></td><td>dep_rel</td><td>deps</td><td>misc</td><td></td><td></td></tr><tr><td>1</td><td>root</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\</td><td></td><td></td></tr><tr><td>2</td><td>root</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\</td><td></td><td></td></tr><tr><td>3</td><td>root</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\</td><td></td><td></td></tr><tr><td>4</td><td>root</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\</td><td></td><td></td></tr><tr><td>5</td><td>root</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\</td><td></td><td></td></tr><tr><td>6</td><td>root</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\</td><td></td><td></td></tr><tr><td>7</td><td>root</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\</td><td></td><td></td></tr><tr><td>8</td><td>root</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\</td><td></td><td></td></tr><tr><td>9</td><td>root</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\</td><td></td><td></td></tr><tr><td>10</td><td>root</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\</td><td></td><td></td></tr></tbody></table>

```
## parse text
```

```
web_pages_txt_corpus_sent <- udpipe_ annotate(m_english, x = web_pages_txt_corpus[[1]])
as.data.frame()
```

```
## inspect
```

```
head(web_pages_txt_corpus_sent)
```
5.8.6.5.3 Dependency parsing

<table><thead><tr><th>doc_id</th><th>paragraph_id</th><th>sentence_id</th><th>feats</th></tr></thead><tbody><tr><td>1</td><td>doc1</td><td>1</td><td>1</td></tr><tr><td>2</td><td>doc1</td><td>1</td><td>1</td></tr><tr><td>3</td><td>doc1</td><td>1</td><td>1</td></tr><tr><td>4</td><td>doc1</td><td>1</td><td>1</td></tr><tr><td>5</td><td>doc1</td><td>1</td><td>2</td></tr><tr><td>6</td><td>doc1</td><td>1</td><td>2</td></tr></tbody></table><table><thead><tr><th>token_id</th><th>token</th><th>lemma</th><th>upos</th><th>xpos</th><th>misc</th></tr></thead><tbody><tr><td>1</td><td>1</td><td>Skip</td><td>skip</td><td>VERB</td><td>Mood=Imp|VerbForm=Fin</td></tr><tr><td>2</td><td>2</td><td>to</td><td>to</td><td>PART</td><td>&lt;NA&gt;</td></tr><tr><td>3</td><td>3</td><td>content</td><td>contend</td><td>VERB</td><td>VerbForm=Inf</td></tr><tr><td>4</td><td>4</td><td>ggplot24.0.1</td><td>ggplot24.0.1</td><td>NOUN</td><td>Number=Sing</td></tr><tr><td>5</td><td>1</td><td>Get</td><td>get</td><td>VERB</td><td>Mood=Imp|VerbForm=Fin</td></tr><tr><td>6</td><td>2</td><td>started</td><td>start</td><td>VERB</td><td>VBN Tense=Past|VerbForm=Part</td></tr></tbody></table><table><thead><tr><th>head_token_id</th><th>dep_rel</th><th>deps</th><th>misc</th></tr></thead><tbody><tr><td>0</td><td>root</td><td>&lt;NA&gt;</td><td>&lt;NA&gt;</td></tr><tr><td>3</td><td>mark</td><td>&lt;NA&gt;</td><td>&lt;NA&gt;</td></tr><tr><td>1</td><td>xcomp</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td></tr><tr><td>3</td><td>obj</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td></tr><tr><td>0</td><td>root</td><td>&lt;NA&gt;</td><td>&lt;NA&gt;</td></tr><tr><td>29</td><td>amod</td><td>&lt;NA&gt;</td><td>SpacesAfter=\\n</td></tr></tbody></table>

web_pages_txt_corpus_sent_dplot <- textplot_dependencyparser(web_pages_txt_corpus_sent, size = 3)

## show plot

web_pages_txt_corpus_sent_dplot
Dependency Parser

tokenisation, parts of speech tagging & dependency relations

<figure><img src="image_155.png" /></figure>

5.8.6.6 Feature co-occurrence matrix (FCM)

Feature co-occurrence matrix (FCM) records the number of co-occurrences of
tokens

web_pages_txt_corpus DFmat <- dfm(web_pages_txt_corpus tok_no_punct)
web_pages_txt_corpus DFmat Trim <- dfm Trim(web_pages_txt_corpus DFmat, min_termfreq =
topfeatures_web_pages_txt_corpus <- topfeatures(web_pages_txt_corpus DFmat Trim)
topfeatures_web_pages_txt_corpus

5.8.6.6.1 Computing number of co-occurrences

#> the to a of < and version 1
#> 271 187 147 138 132 91 91 79 74 72

nfeat(web_pages_txt_corpus DFmat Trim)

## [1] 260

web_pages_txt_corpus fcmat <- fcm(web_pages_txt_corpus DFmat Trim)
web_pages_txt_corpus fcmat

5.8.6.6.2 Features co-occurrences

## Feature co-occurrence matrix of: 260 by 260 features.

## features
## features skip to content get started reference news releases version 2.0.0
## skip 0 147 9 23 10 9 9 10 74 8
## to 0 1459 147 427 157 147 147 183 1263 252
## content 0 0 0 23 10 9 9 10 74 8
## get 0 0 0 23 25 23 23 26 193 25
## started 0 0 0 0 1 10 10 11 81 8
## reference 0 0 0 0 0 0 9 10 74 8
## news 0 0 0 0 0 0 0 10 74 8
## releases 0 0 0 0 0 0 0 1 82 14
## version 0 0 0 0 0 0 0 0 301 67
## 2.0.0 0 0 0 0 0 0 0 0 0 15
## [ reached max_nfeat ... 250 more features, reached max_nfeat ... 250 more features ]

## 5.9 session info

sessionInfo()

## R version 4.5.2 (2025-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8
## [2] LC_CTYPE=English_United Kingdom.utf8
## [3] LC_MONETARY=English_United Kingdom.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.utf8
##
## time zone: Europe/Paris
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggraph_2.2.2 textplot_0.2.2
## [3] udpipe_0.8.14 quanteda.textplots_0.96.1
## [5] stopwords_2.3 SnowballC_0.7.1
## [7] tm_0.7-16 NLP_0.3-2
## [9] readtext_0.92.1 quanteda_4.3.1
## [11] gutenbergr_0.3.0 textdata_0.4.5
## [13] proustr_0.4.0 jane Austen r_1.0.0
## [15] rvest_1.0.5      tidytext_0.4.3
## [17] lattice_0.22-7    paletteer_1.6.0
## [19] ggstatsplot_0.13.3 ggstats_0.11.0
## [21] itsadug_2.4.1     plotfunctions_1.4
## [23] mgcv_1.9-4       nlme_3.1-168
## [25] lme4_1.1-37      Matrix_1.7-4
## [27] faux_1.2.3       car_3.1-3
## [29] carData_3.0-5     parameters_0.28.3
## [31] datawizard_1.3.0   effectsize_1.0.1
## [33] pwr_1.3-0        htmltools_0.5.8.1
## [35] sjPlot_2.9.0      DHARMa_0.4.7
## [37] ordinal_2023.12-4.1 psycho_0.6.1
## [39] PresenceAbsence_1.1.11 ggsignif_0.6.4
corrplot_0.95
## [41] emmeans_2.0.0
## [43] Hmisc_5.2-4
## [45] broom_1.0.10
## [47] gtsummary_2.4.0
## [49] summarytools_1.1.4 phonR_1.0-7
## [51] languageR_1.6
## [53] forcats_1.0.1
## [55] dplyr_1.1.4
## [57] readr_2.1.6
## [59] tibble_3.3.0
## [61] tidyverse_2.0.0

##

## loaded via a namespace (and not attached):

## [1] fs_1.6.6          matrixStats_1.5.0      bitops_1.0-9
## [4] httr_1.4.7        RColorBrewer_1.1-3      insight_1.4.3
## [7] streamR_0.4.5      numDeriv_2016.8-1.1      tools_4.5.2
## [10] backports_1.5.0   sjlabelled_1.2.0        utf8_1.2.6
## [13] R6_2.6.1         statsExpressions_1.7.1    litedown_0.8
## [16] withr_3.0.2       sp_2.2-0               splancs_2.01-45
## [19] gridExtra_2.3     cli_3.6.5               performance_0.15.2
## [22] gt_1.1.0         sandwich_3.1-1           labeling_0.4.3
## [25] slam_0.1-55      prismatic_1.1.2          mvtnorm_1.3-3
## [28] S7_0.2.1         askpass_1.2.1            commonmark_2.0.0
## [31] foreign_0.8-90    parallelly_1.45.1         attempt_0.3.1
## [34] labelled_2.16.0   rstudioapi_0.17.1        generics_0.1.4
## [37] vroom_1.6.6       distributional_0.5.0     abind_1.4-8
## [40] lifecycle_1.0.4    multcomp_1.4-29          yaml_2.3.10
## [43] snakecase_0.11.1  grid_4.5.2               crayon_1.5.3
## [46] haven_2.5.5       magick_2.9.0             zeallot_0.2.0
## [49] pillar_1.11.1     tcltk_4.5.2               rjson_0.2.23
## [52] boot_1.3-32      ndjson_0.9.1              estimability_1.5.1
## [55] codetools_0.2-20  fastmatch_1.1-6          glue_1.8.0
## [58] pdftools_3.6.0    qpdf_1.4.1               data.table_1.17.8
<table>
  <tr>
    <td>[61]</td>
    <td>broomOT helpers_1.22.0</td>
    <td>vctrs_0.6.5</td>
    <td>Rdpack_2.6.4</td>
  </tr>
  <tr>
    <td>[64]</td>
    <td>gtable_0.3.6</td>
    <td>rematch2_2.1.2</td>
    <td>cachem_1.1.0</td>
  </tr>
  <tr>
    <td>[67]</td>
    <td>xfun_0.54</td>
    <td>rbibutils_2.4</td>
    <td>tidygraph_1.3.1</td>
  </tr>
  <tr>
    <td>[70]</td>
    <td>correlation_0.8.8</td>
    <td>coda_0.19-4.1</td>
    <td>reformulas_0.4.2</td>
  </tr>
  <tr>
    <td>[73]</td>
    <td>survival_3.8-3</td>
    <td>tinytex_0.58</td>
    <td>TH.data_1.1-5</td>
  </tr>
  <tr>
    <td>[76]</td>
    <td>gap_1.6</td>
    <td>bit64_4.6.0-1</td>
    <td>rpart_4.1.24</td>
  </tr>
  <tr>
    <td>[79]</td>
    <td>colorspace_2.1-2</td>
    <td>nnet_7.3-20</td>
    <td>tidyselect_1.2.1</td>
  </tr>
  <tr>
    <td>[82]</td>
    <td>bit_4.6.0</td>
    <td>compiler_4.5.2</td>
    <td>curl_7.0.0</td>
  </tr>
  <tr>
    <td>[85]</td>
    <td>htmlTable_2.4.3</td>
    <td>xml2_1.5.0</td>
    <td>bayestestR_0.17.0</td>
  </tr>
  <tr>
    <td>[88]</td>
    <td>bookdown_0.45</td>
    <td>checkmate_2.3.3</td>
    <td>scales_1.4.0</td>
  </tr>
  <tr>
    <td>[91]</td>
    <td>quadprog_1.5-8</td>
    <td>digest_0.6.39</td>
    <td>minqa_1.2.8</td>
  </tr>
  <tr>
    <td>[94]</td>
    <td>r markdown_2.30</td>
    <td>pkgconfig_2.0.3</td>
    <td>base64enc_0.1-3</td>
  </tr>
  <tr>
    <td>[97]</td>
    <td>fastmap_1.2.0</td>
    <td>rlang_1.1.6</td>
    <td>htmlwidgets_1.6.4</td>
  </tr>
  <tr>
    <td>[100]</td>
    <td>pryr_0.1.6</td>
    <td>farver_2.1.2</td>
    <td>zoo_1.8-14</td>
  </tr>
  <tr>
    <td>[103]</td>
    <td>broomOT mixed_0.2.9.6</td>
    <td>tokenizers_0.3.0</td>
    <td>RCurl_1.98-1.17</td>
  </tr>
  <tr>
    <td>[106]</td>
    <td>magrittr_2.0.4</td>
    <td>rapportools_1.2</td>
    <td>Formula_1.2-5</td>
  </tr>
  <tr>
    <td>[109]</td>
    <td>patchwork_1.3.2</td>
    <td>Rcpp_1.1.0</td>
    <td>viridis_0.6.5</td>
  </tr>
  <tr>
    <td>[112]</td>
    <td>ucminf_1.2.2</td>
    <td>furr r_0.3.1</td>
    <td>stringi_1.8.7</td>
  </tr>
  <tr>
    <td>[115]</td>
    <td>MASS_7.3-65</td>
    <td>plyr_1.8.9</td>
    <td>parallel_4.5.2</td>
  </tr>
  <tr>
    <td>[118]</td>
    <td>listenv_0.10.0</td>
    <td>ggrepel_0.9.6</td>
    <td>sjmisc_2.8.11</td>
  </tr>
  <tr>
    <td>[121]</td>
    <td>graphlayouts_1.2.2</td>
    <td>ggeffects_2.3.1</td>
    <td>splines_4.5.2</td>
  </tr>
  <tr>
    <td>[124]</td>
    <td>pander_0.6.6</td>
    <td>hms_1.1.4</td>
    <td>igraph_2.2.1</td>
  </tr>
  <tr>
    <td>[127]</td>
    <td> markdown_2.0</td>
    <td>reshape2_1.4.5</td>
    <td>rstantools_2.5.0</td>
  </tr>
  <tr>
    <td>[130]</td>
    <td>evaluate_1.0.5</td>
    <td>RcppParallel_5.1.11-1</td>
    <td>tweenr_2.0.3</td>
  </tr>
  <tr>
    <td>[133]</td>
    <td>nloptr_2.2.1</td>
    <td>tzdb_0.5.0</td>
    <td>selectr_0.5-0</td>
  </tr>
  <tr>
    <td>[136]</td>
    <td>cards_0.7.0</td>
    <td>polyclip_1.10-7</td>
    <td>future_1.68.0</td>
  </tr>
  <tr>
    <td>[139]</td>
    <td>ggforce_0.5.0</td>
    <td>gap datasets_0.0.6</td>
    <td>xtable_1.8-4</td>
  </tr>
  <tr>
    <td>[142]</td>
    <td>viridisLite_0.4.2</td>
    <td>readMDTable_0.3.2</td>
    <td>memoise_2.0.1</td>
  </tr>
  <tr>
    <td>[145]</td>
    <td>cluster_2.1.8.1</td>
    <td>timechange_0.3.0</td>
    <td>globals_0.18.0</td>
  </tr>
</table>
## 6.2 jane austen

### 6.2.1 Import into Global Environment

Adding data from the first book into the Global Environment

```
data(sensesensibility)
#data(prideprejudice)
#data(mansfieldpark)
#data(emma)
#data(northangerabbey)
#data(persuasion)
```

### 6.2.2 Look into data

And we can get the top 60 rows from "sensesensibility"

```
sensesensibility %>%
  head(n = 60)

## [1] "SENSE AND SENSIBILITY"
## [2]"
## [3] "by Jane Austen"
## [4]"
## [5] "(1811)"
## [6]"
## [7]"
## [8]"
## [9]"
## [10] "CHAPTER 1"
## [11]"
## [12]"
## [13] "The family of Dashwood had long been settled in Sussex. Their estate"
## [14] "was large, and their residence was at Norland Park, in the centre of"
## [15] "their property, where, for many generations, they had lived in so"
## [16] "respectable a manner as to engage the general good opinion of their"
## [17] "surrounding acquaintance. The late owner of this estate was a single"
## [18] "man, who lived to a very advanced age, and who for many years of his"
## [19] "life, had a constant companion and housekeeper in his sister. But her"
## [20] "death, which happened ten years before his own, produced a great"
## [21] "alteration in his home; for to supply her loss, he invited and received"
## [22] "into his house the family of his nephew Mr. Henry Dashwood, the legal"
## [23] "inheritor of the Norland estate, and the person to whom he intended to"
## [24] "bequeath it. In the society of his nephew and niece, and their"
## [25] "children, the old Gentleman's days were comfortably spent. His"
## [26] "attachment to them all increased. The constant attention of Mr. and"
## [27] "Mrs. Henry Dashwood to his wishes, which proceeded not merely from"
```
[28] "interest, but from goodness of heart, gave him every degree of solid"

[29] "comfort which his age could receive; and the cheerfulness of the"

[30] "children added a relish to his existence."

[31] ""

[32] "By a former marriage, Mr. Henry Dashwood had one son: by his present"

[33] "lady, three daughters. The son, a steady respectable young man, was"

[34] "amply provided for by the fortune of his mother, which had been large,"

[35] "and half of which devolved on him on his coming of age. By his own"

[36] "marriage, likewise, which happened soon afterwards, he added to his"

[37] "wealth. To him therefore the succession to the Norland estate was not"

[38] "so really important as to his sisters; for their fortune, independent"

[39] "of what might arise to them from their father's inheriting that"

[40] "property, could be but small. Their mother had nothing, and their"

[41] "father only seven thousand pounds in his own disposal; for the"

[42] "remaining moiety of his first wife's fortune was also secured to her"

[43] "child, and he had only a life-interest in it."

[44] ""

[45] "The old gentleman died: his will was read, and like almost every other"

[46] "will, gave as much disappointment as pleasure. He was neither so"

[47] "unjust, nor so ungrateful, as to leave his estate from his nephew;--but"

[48] "he left it to him on such terms as destroyed half the value of the"

[49] "bequest. Mr. Dashwood had wished for it more for the sake of his wife"

[50] "and daughters than for himself or his son;--but to his son, and his"

[51] "son's son, a child of four years old, it was secured, in such a way, as"

[52] "to leave to himself no power of providing for those who were most dear"

[53] "to him, and who most needed a provision by any charge on the estate, or"

[54] "by any sale of its valuable woods. The whole was tied up for the"

[55] "benefit of this child, who, in occasional visits with his father and"

[56] "mother at Norland, had so far gained on the affections of his uncle, by"

[57] "such attractions as are by no means unusual in children of two or three"

[58] "years old; an imperfect articulation, an earnest desire of having his"

[59] "own way, many cunning tricks, and a great deal of noise, as to outweigh"

[60] "all the value of all the attention which, for years, he had received"

### 6.2.3 Transform to a data frame

```
sensesensibility DF <- sensesensibility %>
  data.frame()
```

```
sensesensibility DF <- sensesensibility DF[-c(1:12),]
```

```
sensesensibility DF %>
  head(60)
```

[1] "The family of Dashwood had long been settled in Sussex. Their estate"

[2] "was large, and their residence was at Norland Park, in the centre of"

[3] "their property, where, for many generations, they had lived in so"

[4] "respectable a manner as to engage the general good opinion of their"
[5] "surrounding acquaintance. The late owner of this estate was a single"
[6] "man, who lived to a very advanced age, and who for many years of his"
[7] "life, had a constant companion and housekeeper in his sister. But her"
[8] "death, which happened ten years before his own, produced a great"
[9] "alteration in his home; for to supply her loss, he invited and received"
[10] "into his house the family of his nephew Mr. Henry Dashwood, the legal"
[11] "inheritor of the Norland estate, and the person to whom he intended to"
[12] "bequeath it. In the society of his nephew and niece, and their"
[13] "children, the old Gentleman's days were comfortably spent. His"
[14] "attachment to them all increased. The constant attention of Mr. and"
[15] "Mrs. Henry Dashwood to his wishes, which proceeded not merely from"
[16] "interest, but from goodness of heart, gave him every degree of solid"
[17] "comfort which his age could receive; and the cheerfulness of the"
[18] "children added a relish to his existence."
[19] ""
[20] "By a former marriage, Mr. Henry Dashwood had one son: by his present"
[21] "lady, three daughters. The son, a steady respectable young man, was"
[22] "amply provided for by the fortune of his mother, which had been large,"
[23] "and half of which devolved on him on his coming of age. By his own"
[24] "marriage, likewise, which happened soon afterwards, he added to his"
[25] "wealth. To him therefore the succession to the Norland estate was not"
[26] "so really important as to his sisters; for their fortune, independent"
[27] "of what might arise to them from their father's inheriting that"
[28] "property, could be but small. Their mother had nothing, and their"
[29] "father only seven thousand pounds in his own disposal; for the"
[30] "remaining moiety of his first wife's fortune was also secured to her"
[31] "child, and he had only a life-interest in it."
[32] ""
[33] "The old gentleman died: his will was read, and like almost every other"
[34] "will, gave as much disappointment as pleasure. He was neither so"
[35] "unjust, nor so ungrateful, as to leave his estate from his nephew;--but"
[36] "he left it to him on such terms as destroyed half the value of the"
[37] "bequest. Mr. Dashwood had wished for it more for the sake of his wife"
[38] "and daughters than for himself or his son;--but to his son, and his"
[39] "son's son, a child of four years old, it was secured, in such a way, as"
[40] "to leave to himself no power of providing for those who were most dear"
[41] "to him, and who most needed a provision by any charge on the estate, or"
[42] "by any sale of its valuable woods. The whole was tied up for the"
[43] "benefit of this child, who, in occasional visits with his father and"
[44] "mother at Norland, had so far gained on the affections of his uncle, by"
[45] "such attractions as are by no means unusual in children of two or three"
[46] "years old; an imperfect articulation, an earnest desire of having his"
[47] "own way, many cunning tricks, and a great deal of noise, as to outweigh"
[48] "all the value of all the attention which, for years, he had received"
[49] "from his niece and her daughters. He meant not to be unkind, however,"
[50] "and, as a mark of his affection for the three girls, he left them a"
## [51] "thousand pounds a-piece."
## [52] ""
## [53] "Mr. Dashwood's disappointment was, at first, severe; but his temper was"
## [54] "cheerful and sanguine; and he might reasonably hope to live many years,"
## [55] "and by living economically, lay by a considerable sum from the produce"
## [56] "of an estate already large, and capable of almost immediate"
## [57] "improvement. But the fortune, which had been so tardy in coming, was"
## [58] "his only one twelvemonth. He survived his uncle no longer; and ten"
## [59] "thousand pounds, including the late legacies, was all that remained for"
## [60] "his widow and daughters."

### 6.2.4 Create a corpus

sensesensibility_corpus <- corpus(sensesensibility DF)
print(sensesensibility_corpus)

## Corpus consisting of 12,612 documents.

## text1 :

## "The family of Dashwood had long been settled in Sussex. The..."

## text2 :

## "was large, and their residence was at Norland Park, in the c..."

## text3 :

## "their property, where, for many generations, they had lived ..."

## text4 :

## "respectable a manner as to engage the general good opinion o..."

## text5 :

## "surrounding acquaintance. The late owner of this estate was..."

## text6 :

## "man, who lived to a very advanced age, and who for many year..."

## [ reached max_ndoc ... 12,606 more documents ]

Given that we have many empty lines in this corpus, we clean it by excluding
any empty line.

sensesensibility_corpus <- corpus_subset(sensesensibility_corpus, ntoken(sensesensibility_corpus)

#### 6.2.4.1 Summary

summary(sensesensibility_corpus, 10)
## Corpus consisting of 10592 documents, showing 10 documents:

##

## Text Types Tokens Sentences

<table><tr><td>text1</td><td>13</td><td>13</td><td>2</td></tr><tr><td>text2</td><td>13</td><td>15</td><td>1</td></tr><tr><td>text3</td><td>12</td><td>14</td><td>1</td></tr><tr><td>text4</td><td>12</td><td>12</td><td>1</td></tr><tr><td>text5</td><td>12</td><td>12</td><td>2</td></tr><tr><td>text6</td><td>15</td><td>17</td><td>1</td></tr><tr><td>text7</td><td>14</td><td>14</td><td>2</td></tr><tr><td>text8</td><td>12</td><td>13</td><td>1</td></tr><tr><td>text9</td><td>15</td><td>15</td><td>1</td></tr><tr><td>text10</td><td>13</td><td>15</td><td>1</td></tr></table>

### 6.2.4.2 Accessing parts of corpus

ndoc(sensesensibility_corpus)

## [1] 10592

sensesensibility_corpus[[1]]

## [1] "The family of Dashwood had long been settled in Sussex. Their estate"

## 6.2.5 Tokens

tokens() segments texts in a corpus into tokens (words or sentences) by word boundaries. We can remove punctuations or not

### 6.2.5.1 With punctuations

sensesensibility_corpus_tokens <- tokens(sensesensibility_corpus)
sensesensibility_corpus_tokens

## Tokens consisting of 10,592 documents.

## text1 :

## [1] "The" "family" "of" "Dashwood" "had" "long"
## [7] "been" "settled" "in" "Sussex" "." "Their"
## [ ... and 1 more ]

##

## text2 :

## [1] "was" "large" "," "and" "their" "residence"
## [7] "was" "at" "Norland" "Park" "," "in"
## [ ... and 3 more ]

##

## text3 :

## [1] "their" "property" "," "where" ","
## [6] "for" "many" "generations" "," "they"
## [11] "had" "lived"
## [ ... and 2 more ]
##
## text4 :
## [1] "respectable" "a" "manner" "as" "to"
## [6] "engage" "the" "general" "good" "opinion"
## [11] "of" "their"
##
## text5 :
## [1] "surrounding" "acquaintance" "." "The" "late"
## [6] "owner" "of" "this" "estate" "was"
## [11] "a" "single"
##
## text6 :
## [1] "man" "," "who" "lived" "to" "a"
## [7] "very" "advanced" "age" "," "and" "who"
## [ ... and 5 more ]
##
## [ reached max_ndoc ... 10,586 more documents ]

### 6.2.5.2 Without punctuations

sensesensibility_corpus_tok_no_punct <- tokens(sensesensibility_corpus, remove_punct = TRUE)
sensesensibility_corpus_tok_no_punct

## Tokens consisting of 10,592 documents.

## text1 :
## [1] "The" "family" "of" "Dashwood" "had" "long"
## [7] "been" "settled" "in" "Sussex" "Their" "estate"

##
## text2 :
## [1] "was" "large" "and" "their" "residence" "was"
## [7] "at" "Norland" "Park" "in" "the" "centre"
## [ ... and 1 more ]
##

## text3 :
## [1] "their" "property" "where" "for" "many"
## [6] "generations" "they" "had" "lived" "in"
## [11] "so"

##
## text4 :
## [1] "respectable" "a" "manner" "as" "to"
## [6] "engage" "the" "general" "good" "opinion"
## [11] "of" "their"
##
## text5 :
## [1] "surrounding" "acquaintance" "The" "late" "owner"
## [6] "of" "this" "estate" "was" "a"
## [11] "single"
##
## text6 :
## [1] "man" "who" "lived" "to" "a" "very"
## [7] "advanced" "age" "and" "who" "for" "many"
## [ ... and 3 more ]
##
## [ reached max_ndoc ... 10,586 more documents ]

6.2.6 Stop words

It is best to remove stop words (function/grammatical words) when we use
statistical analyses of a corpus.

sensesensibility_corpus tok no punct no Stop <- tokens_select(sensesensibility_corpus
tok no punct no Stop

## Tokens consisting of 10,592 documents.
## text1 :
## [1] "family" "Dashwood" "settled" "Sussex" "estate"
##
## text2 :
## [1] "residence" "Norland" "Park" "centre"
##
## text3 :
## [1] "property" "generations" "lived"
##
## text4 :
## [1] "respectable" "manner" "engage" "opinion"
##
## text5 :
## [1] "surrounding" "acquaintance" "late" "owner" "estate"
## [6] "single"
##
## text6 :
## [1] "lived" "advanced" "age"
##
## [ reached max_ndoc ... 10,586 more documents ]

6.2.7 Statistical analyses

We can start by providing statistics (whether descriptives or inferential) based
on our corpora.
### 6.2.7.1 Simple frequency analysis

Here we look at obtaining a simple frequency analysis of usage.

#### 6.2.7.1.1 DFM We start by generating a DFM (document-feature matrix)

```
sensesensibility_corpus_tokens_no_punct_no_stop_dfm <- dfm(sensesensibility_corpus_tokens_no_punct_no_stop_dfm, sensesensibility_corpus_tokens_no_punct_no_stop_dfm)
```

## Document-feature matrix of: 10,592 documents, 5,894 features (99.95% sparse) and 0 docvars.

## features

## docs family dashwood settled sussex estate residence norland park centre

<table><tr><td>text1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>text2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>text3</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>text4</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>text5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>text6</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></table>

## features

## docs property

<table><tr><td>text1</td><td>0</td></tr><tr><td>text2</td><td>0</td></tr><tr><td>text3</td><td>1</td></tr><tr><td>text4</td><td>0</td></tr><tr><td>text5</td><td>0</td></tr><tr><td>text6</td><td>0</td></tr></table>

## [ reached max_ndoc ... 10,586 more documents, reached max_nfeat ... 5,884 more features ]

```
sensesensibility_corpus_tokens_no_punct_no_stop_dfm_freq <- textstat_frequency(sensesensibility_corp
sensesensibility_corpus_tokens_no_punct_no_stop_dfm_freq)
```

#### 6.2.7.1.2 Frequencies

<table><thead><tr><th>feature</th><th>frequency</th><th>rank</th><th>docfreq</th><th>group</th></tr></thead><tbody><tr><td>elinor</td><td>623</td><td>1</td><td>621</td><td>all</td></tr><tr><td>marianne</td><td>492</td><td>2</td><td>491</td><td>all</td></tr><tr><td>time</td><td>239</td><td>3</td><td>237</td><td>all</td></tr><tr><td>dashwood</td><td>231</td><td>4</td><td>231</td><td>all</td></tr><tr><td>edward</td><td>220</td><td>5</td><td>220</td><td>all</td></tr><tr><td>sister</td><td>219</td><td>6</td><td>219</td><td>all</td></tr><tr><td>mother</td><td>204</td><td>7</td><td>203</td><td>all</td></tr><tr><td>jennings</td><td>199</td><td>8</td><td>199</td><td>all</td></tr><tr><td>willoughby</td><td>181</td><td>9</td><td>178</td><td>all</td></tr><tr><td>colonel</td><td>165</td><td>10</td><td>165</td><td>all</td></tr><tr><td>lucy</td><td>160</td><td>11</td><td>158</td><td>all</td></tr><tr><td>house</td><td>148</td><td>12</td><td>148</td><td>all</td></tr></tbody></table>
<table>
  <tbody>
    <tr>
      <td>## 13</td>
      <td>john</td>
      <td>148</td>
      <td>12</td>
      <td>148</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 14</td>
      <td>day</td>
      <td>140</td>
      <td>14</td>
      <td>136</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 15</td>
      <td>lady</td>
      <td>138</td>
      <td>15</td>
      <td>138</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 16</td>
      <td>heart</td>
      <td>127</td>
      <td>16</td>
      <td>127</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 17</td>
      <td>sir</td>
      <td>120</td>
      <td>17</td>
      <td>120</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 18</td>
      <td>brandon</td>
      <td>119</td>
      <td>18</td>
      <td>119</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 19</td>
      <td>ferrars</td>
      <td>114</td>
      <td>19</td>
      <td>113</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 20</td>
      <td>replied</td>
      <td>102</td>
      <td>20</td>
      <td>102</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 21</td>
      <td>mind</td>
      <td>100</td>
      <td>21</td>
      <td>100</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 22</td>
      <td>happy</td>
      <td>100</td>
      <td>21</td>
      <td>99</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 23</td>
      <td>left</td>
      <td>98</td>
      <td>23</td>
      <td>97</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 24</td>
      <td>barton</td>
      <td>89</td>
      <td>24</td>
      <td>89</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 25</td>
      <td>hope</td>
      <td>88</td>
      <td>25</td>
      <td>87</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 26</td>
      <td>moment</td>
      <td>87</td>
      <td>26</td>
      <td>87</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 27</td>
      <td>middleton</td>
      <td>87</td>
      <td>26</td>
      <td>87</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 28</td>
      <td>cried</td>
      <td>87</td>
      <td>26</td>
      <td>87</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 29</td>
      <td>town</td>
      <td>86</td>
      <td>29</td>
      <td>85</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 30</td>
      <td>family</td>
      <td>83</td>
      <td>30</td>
      <td>83</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 31</td>
      <td>morning</td>
      <td>83</td>
      <td>30</td>
      <td>83</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 32</td>
      <td>affection</td>
      <td>79</td>
      <td>32</td>
      <td>79</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 33</td>
      <td>heard</td>
      <td>78</td>
      <td>33</td>
      <td>76</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 34</td>
      <td>love</td>
      <td>76</td>
      <td>34</td>
      <td>76</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 35</td>
      <td>letter</td>
      <td>75</td>
      <td>35</td>
      <td>72</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 36</td>
      <td>marianne's</td>
      <td>74</td>
      <td>36</td>
      <td>74</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 37</td>
      <td>feelings</td>
      <td>73</td>
      <td>37</td>
      <td>73</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 38</td>
      <td>brother</td>
      <td>71</td>
      <td>38</td>
      <td>70</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 39</td>
      <td>poor</td>
      <td>71</td>
      <td>38</td>
      <td>71</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 40</td>
      <td>hear</td>
      <td>70</td>
      <td>40</td>
      <td>70</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 41</td>
      <td>spirits</td>
      <td>69</td>
      <td>41</td>
      <td>69</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 42</td>
      <td>told</td>
      <td>69</td>
      <td>41</td>
      <td>68</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 43</td>
      <td>woman</td>
      <td>68</td>
      <td>43</td>
      <td>67</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 44</td>
      <td>subject</td>
      <td>68</td>
      <td>43</td>
      <td>68</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 45</td>
      <td>person</td>
      <td>67</td>
      <td>45</td>
      <td>67</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 46</td>
      <td>pleasure</td>
      <td>66</td>
      <td>46</td>
      <td>66</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 47</td>
      <td>feel</td>
      <td>66</td>
      <td>46</td>
      <td>66</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 48</td>
      <td>happiness</td>
      <td>66</td>
      <td>46</td>
      <td>64</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 49</td>
      <td>palmer</td>
      <td>65</td>
      <td>49</td>
      <td>65</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 50</td>
      <td>acquaintance</td>
      <td>64</td>
      <td>50</td>
      <td>64</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 51</td>
      <td>behaviour</td>
      <td>64</td>
      <td>50</td>
      <td>64</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 52</td>
      <td>opinion</td>
      <td>63</td>
      <td>52</td>
      <td>63</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 53</td>
      <td>comfort</td>
      <td>63</td>
      <td>52</td>
      <td>61</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 54</td>
      <td>engagement</td>
      <td>63</td>
      <td>52</td>
      <td>62</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 55</td>
      <td>elinor's</td>
      <td>62</td>
      <td>55</td>
      <td>62</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 56</td>
      <td>friends</td>
      <td>62</td>
      <td>55</td>
      <td>61</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 57</td>
      <td>visit</td>
      <td>62</td>
      <td>55</td>
      <td>62</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 58</td>
      <td>looked</td>
      <td>61</td>
      <td>58</td>
      <td>61</td>
      <td>all</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>## 59</td>
      <td>cottage</td>
      <td>61</td>
      <td>58</td>
      <td>61</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 60</td>
      <td>speak</td>
      <td>61</td>
      <td>58</td>
      <td>60</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 61</td>
      <td>life</td>
      <td>58</td>
      <td>61</td>
      <td>58</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 62</td>
      <td>attention</td>
      <td>58</td>
      <td>61</td>
      <td>58</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 63</td>
      <td>leave</td>
      <td>58</td>
      <td>61</td>
      <td>57</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 64</td>
      <td>brought</td>
      <td>58</td>
      <td>61</td>
      <td>58</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 65</td>
      <td>short</td>
      <td>57</td>
      <td>65</td>
      <td>57</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 66</td>
      <td>manner</td>
      <td>56</td>
      <td>66</td>
      <td>56</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 67</td>
      <td>eyes</td>
      <td>56</td>
      <td>66</td>
      <td>56</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 68</td>
      <td>continued</td>
      <td>56</td>
      <td>66</td>
      <td>56</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 69</td>
      <td>suppose</td>
      <td>55</td>
      <td>69</td>
      <td>54</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 70</td>
      <td>wished</td>
      <td>54</td>
      <td>70</td>
      <td>54</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 71</td>
      <td>rest</td>
      <td>54</td>
      <td>70</td>
      <td>54</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 72</td>
      <td>married</td>
      <td>54</td>
      <td>70</td>
      <td>51</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 73</td>
      <td>situation</td>
      <td>54</td>
      <td>70</td>
      <td>54</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 74</td>
      <td>norland</td>
      <td>53</td>
      <td>74</td>
      <td>53</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 75</td>
      <td>people</td>
      <td>53</td>
      <td>74</td>
      <td>53</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 76</td>
      <td>sister's</td>
      <td>53</td>
      <td>74</td>
      <td>53</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 77</td>
      <td>friend</td>
      <td>53</td>
      <td>74</td>
      <td>52</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 78</td>
      <td>reason</td>
      <td>53</td>
      <td>74</td>
      <td>52</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 79</td>
      <td>park</td>
      <td>52</td>
      <td>79</td>
      <td>52</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 80</td>
      <td>returned</td>
      <td>52</td>
      <td>79</td>
      <td>52</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 81</td>
      <td>passed</td>
      <td>52</td>
      <td>79</td>
      <td>52</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 82</td>
      <td>voice</td>
      <td>51</td>
      <td>82</td>
      <td>51</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 83</td>
      <td>regard</td>
      <td>50</td>
      <td>83</td>
      <td>50</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 84</td>
      <td>evening</td>
      <td>50</td>
      <td>83</td>
      <td>49</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 85</td>
      <td>body</td>
      <td>50</td>
      <td>83</td>
      <td>48</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 86</td>
      <td>chapter</td>
      <td>49</td>
      <td>86</td>
      <td>49</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 87</td>
      <td>word</td>
      <td>48</td>
      <td>87</td>
      <td>48</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 88</td>
      <td>party</td>
      <td>48</td>
      <td>87</td>
      <td>48</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 89</td>
      <td>street</td>
      <td>48</td>
      <td>87</td>
      <td>48</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 90</td>
      <td>sisters</td>
      <td>47</td>
      <td>90</td>
      <td>45</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 91</td>
      <td>matter</td>
      <td>47</td>
      <td>90</td>
      <td>47</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 92</td>
      <td>minutes</td>
      <td>47</td>
      <td>90</td>
      <td>47</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 93</td>
      <td>obliged</td>
      <td>47</td>
      <td>90</td>
      <td>47</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 94</td>
      <td>mother's</td>
      <td>46</td>
      <td>94</td>
      <td>46</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 95</td>
      <td>determined</td>
      <td>46</td>
      <td>94</td>
      <td>46</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 96</td>
      <td>business</td>
      <td>46</td>
      <td>94</td>
      <td>46</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 97</td>
      <td>doubt</td>
      <td>46</td>
      <td>94</td>
      <td>46</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 98</td>
      <td>power</td>
      <td>45</td>
      <td>98</td>
      <td>45</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 99</td>
      <td>marriage</td>
      <td>44</td>
      <td>99</td>
      <td>44</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 100</td>
      <td>wife</td>
      <td>44</td>
      <td>99</td>
      <td>44</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 101</td>
      <td>account</td>
      <td>44</td>
      <td>99</td>
      <td>44</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 102</td>
      <td>glad</td>
      <td>44</td>
      <td>99</td>
      <td>43</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 103</td>
      <td>days</td>
      <td>43</td>
      <td>103</td>
      <td>43</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 104</td>
      <td>perfectly</td>
      <td>43</td>
      <td>103</td>
      <td>43</td>
      <td>all</td>
    </tr>
  </tbody>
</table>
<table>
  <tr>
    <td>## 105</td>
    <td>called</td>
    <td>43</td>
    <td>103</td>
    <td>43</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 106</td>
    <td>edward's</td>
    <td>43</td>
    <td>103</td>
    <td>43</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 107</td>
    <td>kindness</td>
    <td>42</td>
    <td>107</td>
    <td>42</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 108</td>
    <td>fanny</td>
    <td>42</td>
    <td>107</td>
    <td>42</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 109</td>
    <td>return</td>
    <td>42</td>
    <td>107</td>
    <td>41</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 110</td>
    <td>engaged</td>
    <td>42</td>
    <td>107</td>
    <td>42</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 111</td>
    <td>pounds</td>
    <td>41</td>
    <td>111</td>
    <td>41</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 112</td>
    <td>carriage</td>
    <td>41</td>
    <td>111</td>
    <td>41</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 113</td>
    <td>equally</td>
    <td>40</td>
    <td>113</td>
    <td>39</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 114</td>
    <td>conversation</td>
    <td>40</td>
    <td>113</td>
    <td>40</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 115</td>
    <td>surprise</td>
    <td>40</td>
    <td>113</td>
    <td>40</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 116</td>
    <td>entered</td>
    <td>40</td>
    <td>113</td>
    <td>40</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 117</td>
    <td>talk</td>
    <td>40</td>
    <td>113</td>
    <td>40</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 118</td>
    <td>received</td>
    <td>39</td>
    <td>118</td>
    <td>39</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 119</td>
    <td>hand</td>
    <td>39</td>
    <td>118</td>
    <td>39</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 120</td>
    <td>spent</td>
    <td>38</td>
    <td>120</td>
    <td>38</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 121</td>
    <td>fortune</td>
    <td>38</td>
    <td>120</td>
    <td>38</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 122</td>
    <td>child</td>
    <td>38</td>
    <td>120</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 123</td>
    <td>expected</td>
    <td>38</td>
    <td>120</td>
    <td>38</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 124</td>
    <td>real</td>
    <td>38</td>
    <td>120</td>
    <td>38</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 125</td>
    <td>hours</td>
    <td>38</td>
    <td>120</td>
    <td>38</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 126</td>
    <td>steele</td>
    <td>38</td>
    <td>120</td>
    <td>38</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 127</td>
    <td>settled</td>
    <td>37</td>
    <td>127</td>
    <td>36</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 128</td>
    <td>coming</td>
    <td>37</td>
    <td>127</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 129</td>
    <td>conduct</td>
    <td>37</td>
    <td>127</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 130</td>
    <td>daughter</td>
    <td>37</td>
    <td>127</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 131</td>
    <td>husband</td>
    <td>37</td>
    <td>127</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 132</td>
    <td>months</td>
    <td>37</td>
    <td>127</td>
    <td>35</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 133</td>
    <td>satisfaction</td>
    <td>37</td>
    <td>127</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 134</td>
    <td>silent</td>
    <td>37</td>
    <td>127</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 135</td>
    <td>object</td>
    <td>37</td>
    <td>127</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 136</td>
    <td>door</td>
    <td>37</td>
    <td>127</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 137</td>
    <td>ma'am</td>
    <td>37</td>
    <td>127</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 138</td>
    <td>sat</td>
    <td>37</td>
    <td>127</td>
    <td>37</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 139</td>
    <td>live</td>
    <td>36</td>
    <td>139</td>
    <td>36</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 140</td>
    <td>girl</td>
    <td>36</td>
    <td>139</td>
    <td>36</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 141</td>
    <td>dashwoods</td>
    <td>36</td>
    <td>139</td>
    <td>36</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 142</td>
    <td>impossible</td>
    <td>36</td>
    <td>139</td>
    <td>36</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 143</td>
    <td>pretty</td>
    <td>36</td>
    <td>139</td>
    <td>36</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 144</td>
    <td>jennings's</td>
    <td>36</td>
    <td>139</td>
    <td>36</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 145</td>
    <td>living</td>
    <td>35</td>
    <td>145</td>
    <td>35</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 146</td>
    <td>arrival</td>
    <td>35</td>
    <td>145</td>
    <td>35</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 147</td>
    <td>appeared</td>
    <td>35</td>
    <td>145</td>
    <td>35</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 148</td>
    <td>spite</td>
    <td>35</td>
    <td>145</td>
    <td>35</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 149</td>
    <td>spoke</td>
    <td>35</td>
    <td>145</td>
    <td>35</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 150</td>
    <td>head</td>
    <td>35</td>
    <td>145</td>
    <td>35</td>
    <td>all</td>
  </tr>
</table>
<table>
  <tr>
    <td>## 151</td>
    <td>ready</td>
    <td>35</td>
    <td>145</td>
    <td>35</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 152</td>
    <td>willoughby's</td>
    <td>35</td>
    <td>145</td>
    <td>35</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 153</td>
    <td>london</td>
    <td>35</td>
    <td>145</td>
    <td>35</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 154</td>
    <td>character</td>
    <td>34</td>
    <td>154</td>
    <td>34</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 155</td>
    <td>manners</td>
    <td>34</td>
    <td>154</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 156</td>
    <td>week</td>
    <td>34</td>
    <td>154</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 157</td>
    <td>children</td>
    <td>33</td>
    <td>157</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 158</td>
    <td>attachment</td>
    <td>33</td>
    <td>157</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 159</td>
    <td>sake</td>
    <td>33</td>
    <td>157</td>
    <td>32</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 160</td>
    <td>deal</td>
    <td>33</td>
    <td>157</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 161</td>
    <td>margaret</td>
    <td>33</td>
    <td>157</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 162</td>
    <td>answer</td>
    <td>33</td>
    <td>157</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 163</td>
    <td>ladies</td>
    <td>33</td>
    <td>157</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 164</td>
    <td>night</td>
    <td>33</td>
    <td>157</td>
    <td>32</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 165</td>
    <td>country</td>
    <td>33</td>
    <td>157</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 166</td>
    <td>hour</td>
    <td>33</td>
    <td>157</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 167</td>
    <td>care</td>
    <td>33</td>
    <td>157</td>
    <td>33</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 168</td>
    <td>invitation</td>
    <td>32</td>
    <td>168</td>
    <td>32</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 169</td>
    <td>set</td>
    <td>32</td>
    <td>168</td>
    <td>32</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 170</td>
    <td>assure</td>
    <td>32</td>
    <td>168</td>
    <td>32</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 171</td>
    <td>pleased</td>
    <td>32</td>
    <td>168</td>
    <td>31</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 172</td>
    <td>talked</td>
    <td>32</td>
    <td>168</td>
    <td>32</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 173</td>
    <td>silence</td>
    <td>32</td>
    <td>168</td>
    <td>32</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 174</td>
    <td>son</td>
    <td>31</td>
    <td>174</td>
    <td>30</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 175</td>
    <td>common</td>
    <td>31</td>
    <td>174</td>
    <td>30</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 176</td>
    <td>sense</td>
    <td>31</td>
    <td>174</td>
    <td>31</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 177</td>
    <td>fancy</td>
    <td>31</td>
    <td>174</td>
    <td>31</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 178</td>
    <td>convinced</td>
    <td>31</td>
    <td>174</td>
    <td>31</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 179</td>
    <td>instantly</td>
    <td>31</td>
    <td>174</td>
    <td>31</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 180</td>
    <td>confidence</td>
    <td>31</td>
    <td>174</td>
    <td>31</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 181</td>
    <td>happened</td>
    <td>30</td>
    <td>181</td>
    <td>30</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 182</td>
    <td>wishes</td>
    <td>30</td>
    <td>181</td>
    <td>30</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 183</td>
    <td>comfortable</td>
    <td>30</td>
    <td>181</td>
    <td>30</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 184</td>
    <td>stay</td>
    <td>30</td>
    <td>181</td>
    <td>30</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 185</td>
    <td>judgment</td>
    <td>30</td>
    <td>181</td>
    <td>30</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 186</td>
    <td>natural</td>
    <td>30</td>
    <td>181</td>
    <td>30</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 187</td>
    <td>believed</td>
    <td>30</td>
    <td>181</td>
    <td>30</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 188</td>
    <td>usual</td>
    <td>30</td>
    <td>181</td>
    <td>29</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 189</td>
    <td>delaford</td>
    <td>30</td>
    <td>181</td>
    <td>29</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 190</td>
    <td>robert</td>
    <td>30</td>
    <td>181</td>
    <td>30</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 191</td>
    <td>remained</td>
    <td>29</td>
    <td>191</td>
    <td>29</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 192</td>
    <td>opportunity</td>
    <td>29</td>
    <td>191</td>
    <td>29</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 193</td>
    <td>marry</td>
    <td>29</td>
    <td>191</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 194</td>
    <td>company</td>
    <td>29</td>
    <td>191</td>
    <td>29</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 195</td>
    <td>easily</td>
    <td>29</td>
    <td>191</td>
    <td>29</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 196</td>
    <td>circumstances</td>
    <td>29</td>
    <td>191</td>
    <td>29</td>
    <td>all</td>
  </tr>
</table>
<table>
  <tr>
    <td>## 197</td>
    <td>anxious</td>
    <td>29</td>
    <td>191</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 198</td>
    <td>change</td>
    <td>29</td>
    <td>191</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 199</td>
    <td>walked</td>
    <td>29</td>
    <td>191</td>
    <td>29</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 200</td>
    <td>steeles</td>
    <td>29</td>
    <td>191</td>
    <td>29</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 201</td>
    <td>strong</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 202</td>
    <td>knowledge</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 203</td>
    <td>resolved</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 204</td>
    <td>concern</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 205</td>
    <td>future</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 206</td>
    <td>question</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 207</td>
    <td>countenance</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 208</td>
    <td>misery</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 209</td>
    <td>repeated</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 210</td>
    <td>feeling</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 211</td>
    <td>appearance</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 212</td>
    <td>affair</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 213</td>
    <td>lord</td>
    <td>28</td>
    <td>201</td>
    <td>28</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 214</td>
    <td>daughters</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 215</td>
    <td>handsome</td>
    <td>27</td>
    <td>214</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 216</td>
    <td>notice</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 217</td>
    <td>occasion</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 218</td>
    <td>considered</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 219</td>
    <td>talking</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 220</td>
    <td>expect</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 221</td>
    <td>truth</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 222</td>
    <td>favour</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 223</td>
    <td>taste</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 224</td>
    <td>declared</td>
    <td>27</td>
    <td>214</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 225</td>
    <td>hearing</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 226</td>
    <td>suffered</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 227</td>
    <td>cleveland</td>
    <td>27</td>
    <td>214</td>
    <td>27</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 228</td>
    <td>produced</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 229</td>
    <td>gentleman</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 230</td>
    <td>nature</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 231</td>
    <td>expectation</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 232</td>
    <td>understand</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 233</td>
    <td>acquainted</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 234</td>
    <td>assured</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 235</td>
    <td>mama</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 236</td>
    <td>exceedingly</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 237</td>
    <td>resolution</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 238</td>
    <td>devonshire</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 239</td>
    <td>satisfied</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 240</td>
    <td>surprised</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 241</td>
    <td>fear</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 242</td>
    <td>distress</td>
    <td>26</td>
    <td>228</td>
    <td>26</td>
    <td>all</td>
  </tr>
</table>
<table>
  <tbody>
    <tr>
      <td>## 243</td>
      <td>lucy's</td>
      <td>26</td>
      <td>228</td>
      <td>26</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 244</td>
      <td>favourite</td>
      <td>25</td>
      <td>244</td>
      <td>25</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 245</td>
      <td>advantage</td>
      <td>25</td>
      <td>244</td>
      <td>25</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 246</td>
      <td>money</td>
      <td>25</td>
      <td>244</td>
      <td>25</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 247</td>
      <td>fine</td>
      <td>25</td>
      <td>244</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 248</td>
      <td>scarcely</td>
      <td>25</td>
      <td>244</td>
      <td>25</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 249</td>
      <td>agreeable</td>
      <td>25</td>
      <td>244</td>
      <td>25</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 250</td>
      <td>pain</td>
      <td>25</td>
      <td>244</td>
      <td>25</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 251</td>
      <td>event</td>
      <td>25</td>
      <td>244</td>
      <td>25</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 252</td>
      <td>brandon's</td>
      <td>25</td>
      <td>244</td>
      <td>25</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 253</td>
      <td>meeting</td>
      <td>25</td>
      <td>244</td>
      <td>25</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 254</td>
      <td>bed</td>
      <td>25</td>
      <td>244</td>
      <td>25</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 255</td>
      <td>late</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 256</td>
      <td>society</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 257</td>
      <td>father</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 258</td>
      <td>desire</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 259</td>
      <td>proper</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 260</td>
      <td>delight</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 261</td>
      <td>supposed</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 262</td>
      <td>idea</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 263</td>
      <td>answered</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 264</td>
      <td>painful</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 265</td>
      <td>curiosity</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 266</td>
      <td>joy</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 267</td>
      <td>met</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 268</td>
      <td>dinner</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 269</td>
      <td>fixed</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 270</td>
      <td>forced</td>
      <td>24</td>
      <td>255</td>
      <td>24</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 271</td>
      <td>disappointment</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 272</td>
      <td>earnest</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 273</td>
      <td>income</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 274</td>
      <td>regret</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 275</td>
      <td>knowing</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 276</td>
      <td>true</td>
      <td>23</td>
      <td>271</td>
      <td>22</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 277</td>
      <td>worth</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 278</td>
      <td>strange</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 279</td>
      <td>weeks</td>
      <td>23</td>
      <td>271</td>
      <td>22</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 280</td>
      <td>smile</td>
      <td>23</td>
      <td>271</td>
      <td>22</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 281</td>
      <td>afraid</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 282</td>
      <td>remember</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 283</td>
      <td>formed</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 284</td>
      <td>tears</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 285</td>
      <td>reached</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 286</td>
      <td>table</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 287</td>
      <td>unfortunate</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 288</td>
      <td>bear</td>
      <td>23</td>
      <td>271</td>
      <td>23</td>
      <td>all</td>
    </tr>
  </tbody>
</table>
<table>
  <tr>
    <td>## 289</td>
    <td>constant</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 290</td>
    <td>read</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 291</td>
    <td>danger</td>
    <td>22</td>
    <td>289</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 292</td>
    <td>easy</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 293</td>
    <td>promise</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 294</td>
    <td>affectionate</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 295</td>
    <td>plan</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 296</td>
    <td>prevent</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 297</td>
    <td>meet</td>
    <td>22</td>
    <td>289</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 298</td>
    <td>admiration</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 299</td>
    <td>respect</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 300</td>
    <td>journey</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 301</td>
    <td>window</td>
    <td>22</td>
    <td>289</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 302</td>
    <td>share</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 303</td>
    <td>wrong</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 304</td>
    <td>connection</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 305</td>
    <td>leaving</td>
    <td>22</td>
    <td>289</td>
    <td>22</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 306</td>
    <td>dashwood's</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 307</td>
    <td>temper</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 308</td>
    <td>cold</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 309</td>
    <td>fond</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 310</td>
    <td>equal</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 311</td>
    <td>avoid</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 312</td>
    <td>disposition</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 313</td>
    <td>eager</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 314</td>
    <td>exertion</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 315</td>
    <td>emotion</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 316</td>
    <td>melancholy</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 317</td>
    <td>address</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 318</td>
    <td>speaking</td>
    <td>21</td>
    <td>306</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 319</td>
    <td>conviction</td>
    <td>21</td>
    <td>306</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 320</td>
    <td>mistaken</td>
    <td>21</td>
    <td>306</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 321</td>
    <td>view</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 322</td>
    <td>sitting</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 323</td>
    <td>beauty</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 324</td>
    <td>chance</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 325</td>
    <td>wait</td>
    <td>21</td>
    <td>306</td>
    <td>21</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 326</td>
    <td>lived</td>
    <td>20</td>
    <td>326</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 327</td>
    <td>loss</td>
    <td>20</td>
    <td>326</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 328</td>
    <td>meant</td>
    <td>20</td>
    <td>326</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 329</td>
    <td>arrived</td>
    <td>20</td>
    <td>326</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 330</td>
    <td>eldest</td>
    <td>20</td>
    <td>326</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 331</td>
    <td>times</td>
    <td>20</td>
    <td>326</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 332</td>
    <td>enjoyment</td>
    <td>20</td>
    <td>326</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 333</td>
    <td>friendship</td>
    <td>20</td>
    <td>326</td>
    <td>20</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 334</td>
    <td>particulars</td>
    <td>20</td>
    <td>326</td>
    <td>20</td>
    <td>all</td>
  </tr>
</table>
<table>
  <tbody>
    <tr>
      <td>## 335</td>
      <td>inquiry</td>
      <td>20</td>
      <td>326</td>
      <td>20</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 336</td>
      <td>letters</td>
      <td>20</td>
      <td>326</td>
      <td>20</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 337</td>
      <td>walk</td>
      <td>20</td>
      <td>326</td>
      <td>20</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 338</td>
      <td>elegance</td>
      <td>20</td>
      <td>326</td>
      <td>20</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 339</td>
      <td>delighted</td>
      <td>20</td>
      <td>326</td>
      <td>20</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 340</td>
      <td>soul</td>
      <td>20</td>
      <td>326</td>
      <td>20</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 341</td>
      <td>unhappy</td>
      <td>20</td>
      <td>326</td>
      <td>20</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 342</td>
      <td>compassion</td>
      <td>20</td>
      <td>326</td>
      <td>20</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 343</td>
      <td>lost</td>
      <td>20</td>
      <td>326</td>
      <td>20</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 344</td>
      <td>charlotte</td>
      <td>20</td>
      <td>326</td>
      <td>20</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 345</td>
      <td>estate</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 346</td>
      <td>age</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 347</td>
      <td>required</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 348</td>
      <td>quiet</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 349</td>
      <td>difference</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 350</td>
      <td>circumstance</td>
      <td>19</td>
      <td>345</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 351</td>
      <td>staying</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 352</td>
      <td>indifference</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 353</td>
      <td>pity</td>
      <td>19</td>
      <td>345</td>
      <td>17</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 354</td>
      <td>delightful</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 355</td>
      <td>hoped</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 356</td>
      <td>difficulty</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 357</td>
      <td>discourse</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 358</td>
      <td>stairs</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 359</td>
      <td>weather</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 360</td>
      <td>eye</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 361</td>
      <td>mentioned</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 362</td>
      <td>secret</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 363</td>
      <td>servant</td>
      <td>19</td>
      <td>345</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 364</td>
      <td>tenderness</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 365</td>
      <td>liberty</td>
      <td>19</td>
      <td>345</td>
      <td>19</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 366</td>
      <td>companion</td>
      <td>18</td>
      <td>366</td>
      <td>17</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 367</td>
      <td>capable</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 368</td>
      <td>understanding</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 369</td>
      <td>led</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 370</td>
      <td>gratitude</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 371</td>
      <td>style</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 372</td>
      <td>breakfast</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 373</td>
      <td>sight</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 374</td>
      <td>overcome</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 375</td>
      <td>esteem</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 376</td>
      <td>music</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 377</td>
      <td>praise</td>
      <td>18</td>
      <td>366</td>
      <td>17</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 378</td>
      <td>observation</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 379</td>
      <td>moments</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 380</td>
      <td>attended</td>
      <td>18</td>
      <td>366</td>
      <td>18</td>
      <td>all</td>
    </tr>
  </tbody>
</table>
<table>
  <tr>
    <td>## 381</td>
    <td>calm</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 382</td>
    <td>consequence</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 383</td>
    <td>entrance</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 384</td>
    <td>recollection</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 385</td>
    <td>middletons</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 386</td>
    <td>unable</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 387</td>
    <td>hopes</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 388</td>
    <td>alarm</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 389</td>
    <td>anxiety</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 390</td>
    <td>allenham</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 391</td>
    <td>air</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 392</td>
    <td>honour</td>
    <td>18</td>
    <td>366</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 393</td>
    <td>joined</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 394</td>
    <td>thinking</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 395</td>
    <td>hair</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 396</td>
    <td>relief</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 397</td>
    <td>necessity</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 398</td>
    <td>astonishment</td>
    <td>18</td>
    <td>366</td>
    <td>18</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 399</td>
    <td>amiable</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 400</td>
    <td>a-year</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 401</td>
    <td>parties</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 402</td>
    <td>spirit</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 403</td>
    <td>ease</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 404</td>
    <td>intelligence</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 405</td>
    <td>composure</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 406</td>
    <td>calmness</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 407</td>
    <td>simple</td>
    <td>17</td>
    <td>399</td>
    <td>16</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 408</td>
    <td>warmth</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 409</td>
    <td>attempt</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 410</td>
    <td>worse</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 411</td>
    <td>excuse</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 412</td>
    <td>inclination</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 413</td>
    <td>difficulties</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 414</td>
    <td>cousins</td>
    <td>17</td>
    <td>399</td>
    <td>16</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 415</td>
    <td>bring</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 416</td>
    <td>allowed</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 417</td>
    <td>disappointed</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 418</td>
    <td>pause</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 419</td>
    <td>pray</td>
    <td>17</td>
    <td>399</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 420</td>
    <td>smith</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 421</td>
    <td>charming</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 422</td>
    <td>hands</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 423</td>
    <td>agitation</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 424</td>
    <td>berkeley</td>
    <td>17</td>
    <td>399</td>
    <td>17</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 425</td>
    <td>receive</td>
    <td>16</td>
    <td>425</td>
    <td>16</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 426</td>
    <td>cheerfulness</td>
    <td>16</td>
    <td>425</td>
    <td>16</td>
    <td>all</td>
  </tr>
</table>
<table>
  <tbody>
    <tr>
      <td>## 427</td>
      <td>girls</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 428</td>
      <td>quitted</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 429</td>
      <td>affliction</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 430</td>
      <td>civility</td>
      <td>16</td>
      <td>425</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 431</td>
      <td>carried</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 432</td>
      <td>aware</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 433</td>
      <td>persuaded</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 434</td>
      <td>opinions</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 435</td>
      <td>rich</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 436</td>
      <td>loved</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 437</td>
      <td>grave</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 438</td>
      <td>civil</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 439</td>
      <td>ferrars's</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 440</td>
      <td>john's</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 441</td>
      <td>laughed</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 442</td>
      <td>indifferent</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 443</td>
      <td>influence</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 444</td>
      <td>declare</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 445</td>
      <td>write</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 446</td>
      <td>purpose</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 447</td>
      <td>combe</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 448</td>
      <td>relations</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 449</td>
      <td>harley</td>
      <td>16</td>
      <td>425</td>
      <td>16</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 450</td>
      <td>engage</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 451</td>
      <td>alteration</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 452</td>
      <td>increased</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 453</td>
      <td>gained</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 454</td>
      <td>considerable</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 455</td>
      <td>prospect</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 456</td>
      <td>highly</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 457</td>
      <td>afford</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 458</td>
      <td>altogether</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 459</td>
      <td>imagine</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 460</td>
      <td>pleasant</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 461</td>
      <td>violent</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 462</td>
      <td>unwilling</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 463</td>
      <td>express</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 464</td>
      <td>mutual</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 465</td>
      <td>suspicion</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 466</td>
      <td>disposed</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 467</td>
      <td>delay</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 468</td>
      <td>form</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 469</td>
      <td>offer</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 470</td>
      <td>suffering</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 471</td>
      <td>exeter</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
    <tr>
      <td>## 472</td>
      <td>match</td>
      <td>15</td>
      <td>450</td>
      <td>15</td>
      <td>all</td>
    </tr>
  </tbody>
</table>
<table>
  <tr>
    <td>## 473</td>
    <td>middleton's</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 474</td>
    <td>removed</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 475</td>
    <td>supported</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 476</td>
    <td>smart</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 477</td>
    <td>observed</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 478</td>
    <td>finding</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 479</td>
    <td>prevented</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 480</td>
    <td>tomorrow</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 481</td>
    <td>aye</td>
    <td>15</td>
    <td>450</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 482</td>
    <td>presence</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 483</td>
    <td>note</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 484</td>
    <td>writing</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 485</td>
    <td>duty</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 486</td>
    <td>trust</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 487</td>
    <td>drawing-room</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 488</td>
    <td>expressed</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 489</td>
    <td>utmost</td>
    <td>15</td>
    <td>450</td>
    <td>15</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 490</td>
    <td>respectable</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 491</td>
    <td>invited</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 492</td>
    <td>degree</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 493</td>
    <td>cheerful</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 494</td>
    <td>selfish</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 495</td>
    <td>propriety</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 496</td>
    <td>inconvenience</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 497</td>
    <td>frequently</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 498</td>
    <td>consolation</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 499</td>
    <td>treated</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 500</td>
    <td>dreadful</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 501</td>
    <td>assistance</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 502</td>
    <td>support</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 503</td>
    <td>politeness</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 504</td>
    <td>occurred</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 505</td>
    <td>justice</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 506</td>
    <td>perceive</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 507</td>
    <td>folly</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 508</td>
    <td>wishing</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 509</td>
    <td>encouragement</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 510</td>
    <td>mention</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 511</td>
    <td>relation</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 512</td>
    <td>written</td>
    <td>14</td>
    <td>490</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 513</td>
    <td>understood</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 514</td>
    <td>suffer</td>
    <td>14</td>
    <td>490</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 515</td>
    <td>drew</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 516</td>
    <td>rose</td>
    <td>14</td>
    <td>490</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 517</td>
    <td>engagements</td>
    <td>14</td>
    <td>490</td>
    <td>14</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 518</td>
    <td>marrying</td>
    <td>14</td>
    <td>490</td>
    <td>13</td>
    <td>all</td>
  </tr>
</table>
<table><tr><td>## 519</td><td>yesterday</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 520</td><td>heavy</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 521</td><td>eagerly</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 522</td><td>news</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 523</td><td>amusement</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 524</td><td>spoken</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 525</td><td>proof</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 526</td><td>concerned</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 527</td><td> creature</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 528</td><td>sit</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 529</td><td>fresh</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 530</td><td>morton</td><td>14</td><td>490</td><td>14</td><td>all</td></tr><tr><td>## 531</td><td>intended</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 532</td><td>assurance</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 533</td><td>leisure</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 534</td><td>offence</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 535</td><td>earnestly</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 536</td><td>reflection</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 537</td><td>pressed</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 538</td><td>assist</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 539</td><td>consent</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 540</td><td>trouble</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 541</td><td>secure</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 542</td><td>informed</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 543</td><td>merit</td><td>13</td><td>531</td><td>12</td><td>all</td></tr><tr><td>## 544</td><td>private</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 545</td><td>approbation</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 546</td><td>persuasion</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 547</td><td>probability</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 548</td><td>expression</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 549</td><td>perceived</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 550</td><td>burst</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 551</td><td>calling</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 552</td><td>astonished</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 553</td><td>expense</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 554</td><td>evil</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 555</td><td>comparison</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 556</td><td>solicitude</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 557</td><td>continual</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 558</td><td>vanity</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 559</td><td>extremely</td><td>13</td><td>531</td><td>12</td><td>all</td></tr><tr><td>## 560</td><td>laugh</td><td>13</td><td>531</td><td>12</td><td>all</td></tr><tr><td>## 561</td><td>confess</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 562</td><td>fortnight</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 563</td><td>fellow</td><td>13</td><td>531</td><td>13</td><td>all</td></tr><tr><td>## 564</td><td>employment</td><td>13</td><td>531</td><td>13</td><td>all</td></tr></table>
<table>
  <tr>
    <td>## 565</td>
    <td>o'clock</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 566</td>
    <td>sweet</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 567</td>
    <td>opposition</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 568</td>
    <td>recovered</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 569</td>
    <td>naturally</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 570</td>
    <td>receiving</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 571</td>
    <td>walking</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 572</td>
    <td>grateful</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 573</td>
    <td>dance</td>
    <td>13</td>
    <td>531</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 574</td>
    <td>returning</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 575</td>
    <td>feared</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 576</td>
    <td>health</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 577</td>
    <td>conscience</td>
    <td>13</td>
    <td>531</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 578</td>
    <td>proved</td>
    <td>13</td>
    <td>531</td>
    <td>13</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 579</td>
    <td>death</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 580</td>
    <td>remaining</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 581</td>
    <td>terms</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 582</td>
    <td>affections</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 583</td>
    <td>uncle</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 584</td>
    <td>lay</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 585</td>
    <td>illness</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 586</td>
    <td>promised</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 587</td>
    <td>increase</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 588</td>
    <td>spare</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 589</td>
    <td>sending</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 590</td>
    <td>excellent</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 591</td>
    <td>resemblance</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 592</td>
    <td>grief</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 593</td>
    <td>restored</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 594</td>
    <td>paid</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 595</td>
    <td>horses</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 596</td>
    <td>material</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 597</td>
    <td>consideration</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 598</td>
    <td>unnecessary</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 599</td>
    <td>couple</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 600</td>
    <td>acknowledged</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 601</td>
    <td>superior</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 602</td>
    <td>warm</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 603</td>
    <td>enter</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 604</td>
    <td>beautiful</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 605</td>
    <td>drawn</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 606</td>
    <td>smiling</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 607</td>
    <td>warmly</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 608</td>
    <td>imagination</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 609</td>
    <td>lively</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 610</td>
    <td>greatly</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
</table>
<table>
  <tr>
    <td>## 611</td>
    <td>happen</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 612</td>
    <td>reasonable</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 613</td>
    <td>sudden</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 614</td>
    <td>objection</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 615</td>
    <td>wrote</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 616</td>
    <td>distance</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 617</td>
    <td>hastily</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 618</td>
    <td>explanation</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 619</td>
    <td>remain</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 620</td>
    <td>hills</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 621</td>
    <td>waiting</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 622</td>
    <td>bringing</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 623</td>
    <td>hold</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 624</td>
    <td>procured</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 625</td>
    <td>gay</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 626</td>
    <td>prepared</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 627</td>
    <td>finished</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 628</td>
    <td>censure</td>
    <td>12</td>
    <td>579</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 629</td>
    <td>suddenly</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 630</td>
    <td>raised</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 631</td>
    <td>intimate</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 632</td>
    <td>dislike</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 633</td>
    <td>heartily</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 634</td>
    <td>suspect</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 635</td>
    <td>ignorance</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 636</td>
    <td>tea</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 637</td>
    <td>angry</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 638</td>
    <td>fell</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 639</td>
    <td>bad</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 640</td>
    <td>probable</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 641</td>
    <td>acknowledge</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 642</td>
    <td>exclaimed</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 643</td>
    <td>tone</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 644</td>
    <td>minute</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 645</td>
    <td>forgot</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 646</td>
    <td>moment's</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 647</td>
    <td>palmer's</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 648</td>
    <td>communication</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 649</td>
    <td>miserable</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 650</td>
    <td>wretched</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 651</td>
    <td>consciousness</td>
    <td>12</td>
    <td>579</td>
    <td>12</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 652</td>
    <td>recommended</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 653</td>
    <td>sooner</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 654</td>
    <td>tender</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 655</td>
    <td>eagerness</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 656</td>
    <td>imprudence</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
</table>
<table>
  <tr>
    <td>## 657</td>
    <td>sorrow</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 658</td>
    <td>mistress</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 659</td>
    <td>women</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 660</td>
    <td>spend</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 661</td>
    <td>raise</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 662</td>
    <td>inquiries</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 663</td>
    <td>pleasing</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 664</td>
    <td>intimacy</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 665</td>
    <td>contrary</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 666</td>
    <td>objects</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 667</td>
    <td>ideas</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 668</td>
    <td>choice</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 669</td>
    <td>striking</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 670</td>
    <td>evident</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 671</td>
    <td>drawing</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 672</td>
    <td>picture</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 673</td>
    <td>attempted</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 674</td>
    <td>removal</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 675</td>
    <td>garden</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 676</td>
    <td>cousin</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 677</td>
    <td>explained</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 678</td>
    <td>concluded</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 679</td>
    <td>sigh</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 680</td>
    <td>possession</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 681</td>
    <td>stand</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 682</td>
    <td>design</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 683</td>
    <td>valley</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 684</td>
    <td>village</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 685</td>
    <td>wondered</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 686</td>
    <td>colonel's</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 687</td>
    <td>meaning</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 688</td>
    <td>amazement</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 689</td>
    <td>employed</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 690</td>
    <td>rain</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 691</td>
    <td>caught</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 692</td>
    <td>hurried</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 693</td>
    <td>parlour</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 694</td>
    <td>colour</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 695</td>
    <td>remembrance</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 696</td>
    <td>force</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 697</td>
    <td>reproach</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 698</td>
    <td>spared</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 699</td>
    <td>openly</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 700</td>
    <td>attend</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 701</td>
    <td>judged</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 702</td>
    <td>send</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
</table>
<table>
  <tr>
    <td>## 703</td>
    <td>profession</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 704</td>
    <td>uncertain</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 705</td>
    <td>variety</td>
    <td>11</td>
    <td>652</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 706</td>
    <td>secrecy</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 707</td>
    <td>broken</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 708</td>
    <td>reply</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 709</td>
    <td>stopped</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 710</td>
    <td>public</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 711</td>
    <td>magna</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 712</td>
    <td>absence</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 713</td>
    <td>dared</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 714</td>
    <td>monstrous</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 715</td>
    <td>deceived</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 716</td>
    <td>injured</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 717</td>
    <td>forget</td>
    <td>11</td>
    <td>652</td>
    <td>11</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 718</td>
    <td>sum</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 719</td>
    <td>advice</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 720</td>
    <td>wholly</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 721</td>
    <td>condition</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 722</td>
    <td>visitors</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 723</td>
    <td>neighbourhood</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 724</td>
    <td>request</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 725</td>
    <td>settle</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 726</td>
    <td>furniture</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 727</td>
    <td>absolutely</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 728</td>
    <td>ceased</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 729</td>
    <td>brother's</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 730</td>
    <td>partiality</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 731</td>
    <td>education</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 732</td>
    <td>miles</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 733</td>
    <td>united</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 734</td>
    <td>books</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 735</td>
    <td>mistake</td>
    <td>10</td>
    <td>718</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 736</td>
    <td>thrown</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 737</td>
    <td>sentiments</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 738</td>
    <td>stood</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 739</td>
    <td>indignation</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 740</td>
    <td>attending</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 741</td>
    <td>post</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 742</td>
    <td>offered</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 743</td>
    <td>waited</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 744</td>
    <td>direction</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 745</td>
    <td>depend</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 746</td>
    <td>admire</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 747</td>
    <td>dull</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 748</td>
    <td>safe</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
</table>
<table>
  <tr>
    <td>## 749</td>
    <td>uncomfortable</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 750</td>
    <td>submit</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 751</td>
    <td>assurances</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 752</td>
    <td>fears</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 753</td>
    <td>seated</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 754</td>
    <td>story</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 755</td>
    <td>ay</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 756</td>
    <td>notion</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 757</td>
    <td>endeavouring</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 758</td>
    <td>justify</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 759</td>
    <td>lips</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 760</td>
    <td>light</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 761</td>
    <td>lock</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 762</td>
    <td>attached</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 763</td>
    <td>speech</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 764</td>
    <td>accept</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 765</td>
    <td>apology</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 766</td>
    <td>urged</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 767</td>
    <td>fortitude</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 768</td>
    <td>sleep</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 769</td>
    <td>sunk</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 770</td>
    <td>delicacy</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 771</td>
    <td>forgiven</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 772</td>
    <td>forgive</td>
    <td>10</td>
    <td>718</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 773</td>
    <td>language</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 774</td>
    <td>oxford</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 775</td>
    <td>palmers</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 776</td>
    <td>hurt</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 777</td>
    <td>lucky</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 778</td>
    <td>beau</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 779</td>
    <td>beg</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 780</td>
    <td>borne</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 781</td>
    <td>watched</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 782</td>
    <td>fancied</td>
    <td>10</td>
    <td>718</td>
    <td>10</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 783</td>
    <td>sussex</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 784</td>
    <td>property</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 785</td>
    <td>advanced</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 786</td>
    <td>charge</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 787</td>
    <td>produce</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 788</td>
    <td>twelvemonth</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 789</td>
    <td>learn</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 790</td>
    <td>abilities</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 791</td>
    <td>sensibility</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 792</td>
    <td>continuance</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 793</td>
    <td>observe</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
  <tr>
    <td>## 794</td>
    <td>servants</td>
    <td>9</td>
    <td>783</td>
    <td>9</td>
    <td>all</td>
  </tr>
</table>
<table><tr><td>## 795</td><td>unpleasant</td><td>9 783</td><td>9 all</td></tr><tr><td>## 796</td><td>undoubtedly</td><td>9 783</td><td>9 all</td></tr><tr><td>## 797</td><td>pay</td><td>9 783</td><td>9 all</td></tr><tr><td>## 798</td><td>moved</td><td>9 783</td><td>9 all</td></tr><tr><td>## 799</td><td>intentions</td><td>9 783</td><td>9 all</td></tr><tr><td>## 800</td><td>spot</td><td>9 783</td><td>9 all</td></tr><tr><td>## 801</td><td>impatient</td><td>9 783</td><td>9 all</td></tr><tr><td>## 802</td><td>doubted</td><td>9 783</td><td>9 all</td></tr><tr><td>## 803</td><td>believing</td><td>9 783</td><td>9 all</td></tr><tr><td>## 804</td><td>introduced</td><td>9 783</td><td>9 all</td></tr><tr><td>## 805</td><td>depended</td><td>9 783</td><td>9 all</td></tr><tr><td>## 806</td><td>reserve</td><td>9 783</td><td>9 all</td></tr><tr><td>## 807</td><td>merits</td><td>9 783</td><td>9 all</td></tr><tr><td>## 808</td><td>separation</td><td>9 783</td><td>9 all</td></tr><tr><td>## 809</td><td>frequent</td><td>9 783</td><td>9 all</td></tr><tr><td>## 810</td><td>lover</td><td>9 783</td><td>9 all</td></tr><tr><td>## 811</td><td>satisfy</td><td>9 783</td><td>9 all</td></tr><tr><td>## 812</td><td>seat</td><td>9 783</td><td>9 all</td></tr><tr><td>## 813</td><td>draw</td><td>9 783</td><td>9 all</td></tr><tr><td>## 814</td><td>matters</td><td>9 783</td><td>9 all</td></tr><tr><td>## 815</td><td>direct</td><td>9 783</td><td>9 all</td></tr><tr><td>## 816</td><td>offended</td><td>9 783</td><td>9 all</td></tr><tr><td>## 817</td><td>dearest</td><td>9 783</td><td>9 all</td></tr><tr><td>## 818</td><td>concealed</td><td>9 783</td><td>9 all</td></tr><tr><td>## 819</td><td>delicate</td><td>9 783</td><td>9 all</td></tr><tr><td>## 820</td><td>started</td><td>9 783</td><td>9 all</td></tr><tr><td>## 821</td><td>ashamed</td><td>9 783</td><td>9 all</td></tr><tr><td>## 822</td><td>laughing</td><td>9 783</td><td>9 all</td></tr><tr><td>## 823</td><td>behaved</td><td>9 783</td><td>9 all</td></tr><tr><td>## 824</td><td>result</td><td>9 783</td><td>9 all</td></tr><tr><td>## 825</td><td>distant</td><td>9 783</td><td>9 all</td></tr><tr><td>## 826</td><td>sufficient</td><td>9 783</td><td>9 all</td></tr><tr><td>## 827</td><td>quit</td><td>9 783</td><td>9 all</td></tr><tr><td>## 828</td><td>service</td><td>9 783</td><td>9 all</td></tr><tr><td>## 829</td><td>arose</td><td>9 783</td><td>9 all</td></tr><tr><td>## 830</td><td>determine</td><td>9 783</td><td>9 all</td></tr><tr><td>## 831</td><td>agreed</td><td>9 783</td><td>9 all</td></tr><tr><td>## 832</td><td>dine</td><td>9 783</td><td>9 all</td></tr><tr><td>## 833</td><td>confined</td><td>9 783</td><td>9 all</td></tr><tr><td>## 834</td><td>desirous</td><td>9 783</td><td>9 all</td></tr><tr><td>## 835</td><td>encouraging</td><td>9 783</td><td>9 all</td></tr><tr><td>## 836</td><td>active</td><td>9 783</td><td>9 all</td></tr><tr><td>## 837</td><td>convince</td><td>9 783</td><td>9 all</td></tr><tr><td>## 838</td><td>slight</td><td>9 783</td><td>9 all</td></tr><tr><td>## 839</td><td>extraordinary</td><td>9 783</td><td>9 all</td></tr><tr><td>## 840</td><td>chair</td><td>9 783</td><td>9 all</td></tr></table>
<table><tr><td>## 841</td><td>sort</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 842</td><td>catching</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 843</td><td>complexion</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 844</td><td>embarrassment</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 845</td><td>softened</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 846</td><td>sincerely</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 847</td><td>guess</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 848</td><td>humour</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 849</td><td>shocking</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 850</td><td>listened</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 851</td><td>conscious</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 852</td><td>bestow</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 853</td><td>plainly</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 854</td><td>uneasiness</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 855</td><td>actions</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 856</td><td>telling</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 857</td><td>resist</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 858</td><td>odd</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 859</td><td>motive</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 860</td><td>laid</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 861</td><td>struck</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 862</td><td>grey</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 863</td><td>beaux</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 864</td><td>fit</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 865</td><td>pardon</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 866</td><td>card</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 867</td><td>attempting</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 868</td><td>god</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 869</td><td>cruel</td><td>9</td><td>783</td><td>8</td><td>all</td></tr><tr><td>## 870</td><td>close</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 871</td><td>parsonage</td><td>9</td><td>783</td><td>9</td><td>all</td></tr><tr><td>## 872</td><td>single</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 873</td><td>supply</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 874</td><td>proceeded</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 875</td><td>goodness</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 876</td><td>existence</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 877</td><td>secured</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 878</td><td>died</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 879</td><td>improvement</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 880</td><td>strength</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 881</td><td>mother-in-law</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 882</td><td>rendered</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 883</td><td>completely</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 884</td><td>reflect</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 885</td><td>prudent</td><td>8</td><td>872</td><td>8</td><td>all</td></tr><tr><td>## 886</td><td>excess</td><td>8</td><td>872</td><td>8</td><td>all</td></tr></table>