# Dataset Card for clmet_3_1

**NOTES**: 
- Some of the annotations in the `class` and `pos` configs are not properly formed. These are indicated with warning messages when the dataset is loaded. 
- In addition to the classes mentioned in the README for the dataset, there is an additional class in the `class` dataset called `QUOT`. As far as I can tell, this is used for tagging all quotation marks
- When the `class` and `pos` configs are loaded, the available class/pos tags are shown at the top

## Dataset Statistics:
The following table summarises the corpus make-up:

|PERIOD    | #authors | #texts         |CQP3.1       | non-PUNC	|
|-----------|----------|---------------------|--------|---------|
|1710-1780   |    51   |   88      |   12,182,064 |  10,415,721|
|1780-1850   |   70     | 99        | 13,300,457  | 11,269,977|
|1850-1920   |   91    | 146       |  14,858,239  | 12,657,159|
|TOTAL       |   212   |  333       |  40,340,760  | 34,342,857|

|GENRE (all tokens):| | | |
|---|---|---|---|
                       | | **1710-1780**|  **1780-1850** | **1850-1920** |
|Narrative fiction   |    5,405,645 | 5,780,352 | 7,561,339 |
|Narrative non-fiction |  2,145,946 | 2,261,485 | 1,097,487 |
|Drama                  |   523,318  |  441,040  |  763,352 |
|Letters                | 1,208,219  |  842,795  |  554,046 |
|Treatise               | 1,263,090 | 1,927,272 | 2,030,210 |
|Other                  | 1,635,846 | 2,047,513 | 2,851,805 |

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description

- **Homepage:** http://fedora.clarin-d.uni-saarland.de/clmet/clmet.html
- **Repository:** [Needs More Information]
- **Paper:** https://icame.info/icame_static/ij29/ij29-page69-82.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Henrik De Smet](https://www.arts.kuleuven.be/ling/func/members/hendrik-desmet/func)

### Dataset Summary

The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�, as an offshoot of a bigger project developing a database of text descriptors (Diller, De Smet & Tyrkk� 2011). CLMET3.1 is a principled collection of public domain texts drawn from various online archiving projects. In total, the corpus contains some 34 million words of running text. It incorporates CLMET, CLMETEV, and CLMET3.0, and has been compiled following roughly the same principles, that is:

- The corpus covers the period 17101920, divided into three 70-year sub-periods.
- The texts making up the corpus have all been written by British and Irish authors who are native speakers of English.
- The corpus never contains more than three texts by the same author.
- The texts within each sub-period have been written by authors born within a correspondingly restricted sub-period.

### Supported Tasks and Leaderboards

- `named-entity-recognition`: Since this dataset is tagged, it can be used for performing NER
- `text-classification`: Each text comes with the date of the text and can be used to perform stylistic classification of texts

### Languages

The text in the dataset is in English. The associated BCP-47 code is `en`

## Dataset Structure

### Data Instances

A `plain` sample looks as follows:

```
{'text': "\nFAME AND THE POET\n \nDRAMATIS PERSONAE�\n \nHarry de Reves , a Poet .\n \n( This name , though of course of French origin , has become anglicised and is pronounced de Reevs . )\n \nDick Prattle , a Lieutenant-Major of the Royal Horse Marines .\n \nFame .\n \nScene\n \nThe Poet 's rooms in London .\nWindows in back .\nA high screen in a corner .\n \nTime : February 30th .\n \nThe Poet is sitting at a table writing .\n \n[ Enter Dick Prattle .\n \nPrattle : Hullo , Harry .\n \nde 
Reves : Hullo , Dick .\nGood Lord , where are you from ?\n \nPrattle ( casually ) : The ends of the earth .\n \nde Reves : Well , I 'm damned !\n \nPrattle : Thought I 'd drop in and see how you were getting on .\n \nde Reves : Well , that 's splendid .\nWhat are you doing in London ?\n \nPrattle : Well , I wanted to see if I could get one or two decent ties to wear - you can get nothing out there - then I thought I 'd have a look and see how London was getting on .\n \nde Reves : Splendid !\nHow 's everybody ?\n \nPrattle : All going strong .\n \nde Reves : That 's good .\n \nPrattle ( seeing paper and ink ) : But what are you doing ?\n \nde Reves : Writing .\n \nPrattle : Writing ?\nI did n't know you wrote .\n \nde Reves : Yes , I 
've taken to it rather .\n \nPrattle : I say - writing 's no good .\nWhat do you write ?\n \nde Reves : Oh , poetry .\n \nPrattle : Poetry !\nGood Lord !\n \nde Reves : Yes , that sort of thing , you know .\n \nPrattle : Good Lord !\nDo you make any money by it ?\n \nde Reves : No .\nHardly any .\n \nPrattle : I say - why do n't you chuck it ?\n \nde Reves : Oh , I do n't know .\nSome people seem to like my stuff , rather .\nThat 's why I go on .\n \nPrattle : I 'd chuck it if there 's no money in it .\n \nde Reves : Ah , but then it 's hardly in your line , is it ?\nYou 'd hardly approve of poetry if there was money in it .\n \nPrattle : Oh , I do n't say that .\nIf I could make as much by poetry as I can by betting I do n't say I would n't try the poetry touch , only - -\n \nde Reves : Only what ?\n \nPrattle : Oh , I do n't know .\nOnly there seems more sense in betting , somehow .\n \nde Reves : Well , yes .\nI suppose it 's easier to tell what an earthly horse is going to 
do , than to tell what Pegasus - -\n \nPrattle : What 's Pegasus ?\n \nde Reves : Oh , the winged horse of poets .\n \nPrattle : I say !\nYou do n't believe in a winged horse , do you ?\n \nde Reves : In our trade we believe in all fabulous things 
.\nThey all represent some large truth to us .\nAn emblem like Pegasus is as real a thing to a poet as a Derby winner would be to you .\n \nPrattle : I say .\n( Give me a cigarette .\nThanks . )\nWhat ?\nThen you 'd believe in nymphs and fauns , and Pan , and all those kind of birds ?\n \nde Reves : Yes .\nYes .\nIn all of them .\n \nPrattle : Good Lord !\n \nde Reves : You believe in the Lord Mayor of London , do n't you ?\n \nPrattle : Yes , of course ; but what has - -\n \nde Reves : Four million people or so made him Lord Mayor , did n't they ?\nAnd he represents to them the wealth and dignity and tradition of - -\n \nPrattle : Yes ; but , I say , what has all this - -\n \nde Reves : Well , he stands for an idea to them , and they made him Lord Mayor , and so he is one ...\n \nPrattle : Well , of course he is .\n \nde Reves : In the same way Pan has been made what he is by millions ; by millions to whom he represents world-old traditions .\n \nPrattle ( rising from his chair and stepping backwards , laughing and looking at the Poet in a kind of assumed wonder ) : I say ... I say ... You old heathen ... but Good Lord ...\n \n[ He bumps into the high screen behind , pushing it back a little .\n \nde Reves : Look out !\nLook out !\n \nPrattle : What ?\nWhat 's the matter ?\n \nde Reves : The screen !\n \nPrattle : Oh , sorry , yes .\nI 'll put it right .\n \n[ He is about to go round behind it .\n \nde Reves : No , do n't go round there .\n \nPrattle : What ?\nWhy not ?\n \nde Reves : Oh , you would n't understand .\n \nPrattle : Would n't understand ?\nWhy , what have you got ?\n \nde Reves : Oh , one of those things ... You would n't understand .\n \nPrattle : Of course I 'd understand .\nLet 's have a look .\n \n[ The Poet walks towards Prattle and the screen .\nHe protests no further .\nPrattle looks round the corner of the screen .\n \nAn altar .\n \nde Reves ( removing the screen altogether ) : That is all .\nWhat do you make of it ?\n \n[ An 
altar of Greek design , shaped like a pedestal , is revealed .\nPapers litter the floor all about it .\n \nPrattle : I say - you always were an untidy devil .\n \nde Reves : Well , what do you make of it ?\n \nPrattle : It reminds me of your room at Eton .\n \nde Reves : My room at Eton ?\n \nPrattle : Yes , you always had papers all over your floor .\n \nde Reves : Oh , yes - -\n \nPrattle : And what are these ?\n \nde Reves : All these are poems ; and this is my altar to Fame .\n \nPrattle : To Fame ?\n \nde Reves : The same that Homer knew .\n \nPrattle : Good Lord !\n \nde Reves : Keats never saw her .\nShelley died too young .\nShe came late at the best of times , now scarcely ever .\n \nPrattle : But , my dear fellow , you do n't mean that you think there really is such a person ?\n \nde Reves : I offer all my songs to her .\n \nPrattle : But you do n't mean you think you could actually see Fame ?\n \nde Reves : We poets personify abstract things , and not poets only but 
sculptors7 and painters too .\nAll the great things of the world are those abstract things .\n \nPrattle : But what I mean is , they 're not really there , like you or me .\n \nde Reves : To us these things are more real than men , they outlive generations , they watch the passing of kingdoms : we go by them like dust ; they are still there , unmoved , unsmiling .\n \nPrattle : But , but , you ca n't think that you could see Fame , you do n't expect to see it ?\n \nde Reves : Not to me .\nNever to me .\nShe of the golden trumpet and Greek dress will never appear to me ... We all have our dreams .\n \nPrattle : I say - what have you been doing all day ?\n \nde Reves : I ?\nOh , only writing a sonnet .\n \nPrattle : Is it a long one ?\n \nde Reves : Not very .\n \nPrattle : About how long is it ?\n \nde Reves : About fourteen lines .\n \nPrattle ( impressively ) : I tell you what it is .\n \nde Reves : Yes ?\n \nPrattle : I tell you what .\nYou 've been overworking yourself .\nI 
once got like that on board the Sandhurst , working for the passing-out exam .\nI got so bad that I could have seen anything .\n \nde Reves : Seen anything ?\n \nPrattle : Lord , yes ; horned pigs , snakes with wings ; anything ; one of your winged horses even .\nThey gave me some stuff called bromide for it .\nYou take a rest .\n \nde Reves : But my dear fellow , you do n't understand at all .\nI merely said that abstract things are to a poet as near and real and visible as one of your bookmakers or barmaids .\n \nPrattle : I know .\nYou take a rest .\n \nde Reves : Well , perhaps I will .\nI 'd come with you to that musical comedy you 're going to see , only I 'm a bit tired after writing this ; it 's a tedious job .\nI 'll come another night .\n \nPrattle : How do you know I 'm going to see a musical comedy ?\n \nde Reves : Well , where would you go ?\nHamlet 's 8 on at the Lord Chamberlain 's .\nYou 're not going there .\n \nPrattle : Do I look like it ?\n \nde Reves : No .\n \nPrattle : Well , you 're quite right .\nI 'm going to see `` The Girl from Bedlam . ''\nSo long .\nI must push off now .\nIt 's getting late .\nYou take a rest .\nDo n't add another line to that sonnet ; fourteen 's quite enough .\nYou take a 
rest .\nDo n't have any dinner to-night , just rest .\nI was like that once myself .\nSo long .\n \nde Reves : So long .\n \n[ Exit Prattle .\nde Reves returns to his table and sits down .\n \nGood old Dick !\nHe 's the same as ever .\nLord , how time passes .\n \nHe takes his pen and his sonnet and makes a few alterations .\n \nWell , that 's finished .\nI ca n't do any more to it .\n \n[ He rises and goes to the screen ; he draws back part of it and goes up to the altar .\nHe is about to place his sonnet reverently at the foot of the altar amongst his other verses .\n \nNo , I will not put it there .\nThis one is worthy of the altar .\n \n[ He places the sonnet upon the altar itself .\n \nIf that sonnet does not give me fame , nothing that I have done before will give it to me , nothing that I ever will do .\n \n[ He replaces the screen and returns to his chair at the table .\nTwilight is coming on .\nHe sits with his elbow on the table , his head on his hand , or however the actor pleases .\n \nWell , well .\nFancy seeing Dick again .\nWell , Dick enjoys his life , so he 's no fool .\nWhat was that he said ?\n`` There 's no money in poetry .\nYou 'd better chuck it . ''\nTen years ' work and what have I to show for it ?\nThe admiration of men who care for poetry , and how many of them are there ?\nThere 's a bigger demand for smoked glasses to look at eclipses of the sun .\nWhy should Fame come to me ?\nHave n't I given up my days for her ?\nThat is enough to keep her away .\nI am a poet ; that is enough reason for her to slight me .\nProud and aloof and cold as marble , what does Fame care for us ?\nYes , Dick is right .\nIt 's a poor game chasing illusions , hunting the intangible , pursuing dreams .\nDreams ?\nWhy , we are ourselves dreams .\n \n[ He leans back in his chair .\n \nWe are such stuff As dreams are made on , and our little life Is rounded with a sleep .\n[ He is silent for a while .\nSuddenly he lifts his head .\n \nMy room at Eton , Dick said .\nAn untidy mess .\n \n[ As he lifts his head and says these words , twilight gives place to broad daylight , merely as a hint that the author of the play may have been mistaken , and the whole thing may have been no more than a poet 
's dream .\n \nSo it was , and it 's an untidy mess there ( looking at screen ) too .\nDick 's right .\nI 'll tidy it up .\nI 'll burn the whole damned heap ,\n \n[ He advances impetuously towards the screen .\n \nevery damned poem that I was ever 
fool enough to waste my time on .\n \n[ He pushes back the screen .\nFame in a Greek dress with a long golden trumpet in her hand is seen standing motionless on the altar like a marble goddess .\n \nSo ... you have come !\n \n[ For a while he stands thunderstruck .\nThen he approaches the altar .\n \nDivine fair lady , you have come .\n \n[ He holds up his hand to her and leads her down from the altar and into the centre of the stage .\nAt whatever moment the actor finds it most convenient , he repossesses himself of the sonnet that he had placed on the altar .\nHe now offers it to Fame .\n \nThis is my sonnet .\nIs it well done ?\n \n[ Fame takes it and reads it in silence , while the Poet watches her rapturously .\n \nFame : You 're a bit of all right .\n \nde Reves : What ?\n \nFame : Some poet .\n \nde Reves : I - I - scarcely ... understand .\n \nFame : You 're IT .\n \nde Reves : But ... it is not possible ... are you she that knew Homer ?\n \nFame : Homer ?\nLord , yes .\nBlind old bat , ' e could n't see a yard .\n \nde Reves : O Heavens !\n \n[ Fame walks beautifully to the window .\nShe opens it and puts her head out .\n \nFame ( in a voice with which a woman in an upper storey would cry for help if the house was well alight ) : Hi !\nHi !\nBoys !\nHi !\nSay , folks !\nHi !\n \n[ The murmur of a gathering crowd is heard .\nFame blows her trumpet .\n \nFame : Hi , he 's a poet !\n( Quickly , over her shoulder . )\nWhat 's your name ?\n \nde Reves : De Reves .\n \nFame : His name 's de Reves .\n \nde Reves : Harry de Reves .\n \nFame : His pals call him Harry .\n \nThe Crowd : Hooray !\nHooray !\nHooray !\n \nFame : Say , what 's your favourite colour ?\n \nde Reves : I ... I ... I do n't quite understand .\n \nFame : Well , which do you like best , green or blue ?\n \nde Reves : Oh - er - blue .\n \n[ She blows her trumpet out of the window .\n \nNo - er - I think green .\n \nFame : Green is his favourite colour .\n \nThe Crowd : Hooray !\nHooray !\nHooray !\n \nFame : ` Ere , tell us something .\nThey want to know all about yer .\n \nde Reves : Would n't 9 you perhaps ... would they care to hear my sonnet , if you would - er ...\n \nFame ( picking up quill ) : Here , what 's this ?\n \nde Reves : Oh , that 's my pen .\n \nFame ( after another blast on her trumpet ) : He writes with a quill .\n \n[ Cheers from the Crowd .\n \nFame ( going to a cupboard ) : Here , what have you got in here ?\n \nde Reves : Oh ... er ... those are my breakfast things .\n \nFame ( finding a dirty plate ) : What have yer had on this one ?\n \nde Reves ( mournfully ) : Oh , eggs and bacon .\n \nFame ( at the window ) : He has eggs and bacon for breakfast .\n \nThe Crowd : Hip hip hip , hooray !\nHip hip hip , hooray !\nHip hip hip , hooray !\nFame : Hi , and what 's this ?\n \nde Reves ( miserably ) : Oh , a golf stick .\n \nFame : He 's a man 's man !\nHe 's a virile man !\nHe 's a manly man !\n \n[ Wild cheers from the Crowd , this time only from women 's voices .\n \nde Reves : Oh , this is terrible .\nThis is terrible .\nThis is terrible .\n \n[ Fame gives another peal on her horn .\nShe is about to speak .\n \nde Reves ( solemnly and mournfully ) : One moment , one moment ...\n \nFame : Well , out with it .\n \nde Reves : For ten years , divine lady , I have worshipped you , offering all my songs ... I find ... I find I am not worthy ...\n \nFame : Oh , you 're all right .\n \nde Reves : No , no , I am not worthy .\nIt can not be .\nIt can not possibly be .\nOthers deserve you more .\nI must say it !\nI can not possibly love you .\nOthers are worthy .\nYou will find others .\nBut I , no , no , no .\nIt can not be .\nIt can not be .\nOh , pardon me , but it must not .\n \n[ Meanwhile Fame has been lighting one of his cigarettes .\nShe sits in a comfortable chair , leans right back , and puts her feet right up on the table amongst the poet 's papers .\n \nOh , I fear I offend you .\nBut - it can not be .\n \nFame : Oh , that 's all right , old bird ; no offence .\nI ai n't going to leave you .\n \nde Reves : But - but - but - I do not understand .\n \nFame : I 've come to stay , I have .\n \n[ She blows a puff of smoke through her trumpet .\n \nCURTAIN .\n", 'genre': 'Drama', 'subgenre': 'drama', 'year': '1919', 'quarter_cent': '1900-1924', 'decade': '1910s', 'title': 'Fame and the poet', 'author': 'Dunsany [Edward John Moreton Drax Plunkett]', 'notes': '', 'comments': 'selected from larger 
file', 'period': '1850-1920', 'id': '317'}
```

A `pos` sample looks as follows:

```
{'text': ['FAME', 'AND', 'THE', 'POET', 'DRAMATIS', 'PERSONAE�', 'Harry', 'de', 'Reves', ',', 'a', 'Poet', '.', '(', 'This', 'name', ',', 'though', 'of', 'course', 'of', 'French', 'origin', ',', 'has', 'become', 'anglicised', 'and', 'is', 'pronounced', 'de', 'Reevs', '.', ')', 'Dick', 'Prattle', ',', 'a', 'Lieutenant-Major', 'of', 'the', 'Royal', 'Horse', 'Marines', '.', 'Fame', '.', 'Scene', 'The', 'Poet', "'s", 'rooms', 'in', 'London', '.', 'Windows', 'in', 'back', '.', 'A', 'high', 'screen', 'in', 'a', 'corner', '.', 'Time', ':', 'February', '30th', '.', 'The', 'Poet', 'is', 'sitting', 'at', 'a', 'table', 'writing', '.', '[', 'Enter', 'Dick', 'Prattle', '.', 'Prattle', ':', 'Hullo', ',', 'Harry', '.', 'de', 'Reves', ':', 'Hullo', ',', 'Dick', '.', 'Good', 'Lord', ',', 'where', 'are', 'you', 'from', '?', 'Prattle', '(', 'casually', ')', ':', 'The', 'ends', 'of', 'the', 'earth', '.', 'de', 'Reves', ':', 'Well', ',', 'I', "'m", 'damned', '!', 'Prattle', ':', 'Thought', 'I', "'d", 'drop', 'in', 'and', 'see', 'how', 'you', 'were', 'getting', 'on', '.', 'de', 'Reves', ':', 'Well', ',', 'that', "'s", 'splendid', '.', 'What', 'are', 'you', 'doing', 'in', 'London', '?', 'Prattle', ':', 'Well', ',', 'I', 'wanted', 'to', 'see', 
'if', 'I', 'could', 'get', 'one', 'or', 'two', 'decent', 'ties', 'to', 'wear', '-', 'you', 'can', 'get', 'nothing', 'out', 'there', '-', 'then', 'I', 'thought', 'I', "'d", 'have', 'a', 'look', 'and', 'see', 'how', 'London', 'was', 'getting', 'on', 
'.', 'de', 'Reves', ':', 'Splendid', '!', 'How', "'s", 'everybody', '?', 'Prattle', ':', 'All', 'going', 'strong', '.', 'de', 'Reves', ':', 'That', "'s", 'good', '.', 'Prattle', '(', 'seeing', 'paper', 'and', 'ink', ')', ':', 'But', 'what', 'are', 
'you', 'doing', '?', 'de', 'Reves', ':', 'Writing', '.', 'Prattle', ':', 'Writing', '?', 'I', 'did', "n't", 'know', 'you', 'wrote', '.', 'de', 'Reves', ':', 'Yes', ',', 'I', "'ve", 'taken', 'to', 'it', 'rather', '.', 'Prattle', ':', 'I', 'say', '-', 'writing', "'s", 'no', 'good', '.', 'What', 'do', 'you', 'write', '?', 'de', 'Reves', ':', 'Oh', ',', 'poetry', '.', 'Prattle', ':', 'Poetry', '!', 'Good', 'Lord', '!', 'de', 'Reves', ':', 'Yes', ',', 'that', 'sort', 'of', 'thing', ',', 'you', 'know', '.', 'Prattle', ':', 'Good', 'Lord', '!', 'Do', 'you', 'make', 'any', 'money', 'by', 'it', '?', 'de', 'Reves', ':', 'No', '.', 'Hardly', 'any', '.', 'Prattle', ':', 'I', 'say', '-', 'why', 'do', "n't", 'you', 'chuck', 'it', '?', 'de', 'Reves', ':', 'Oh', ',', 'I', 'do', "n't", 'know', '.', 'Some', 'people', 'seem', 'to', 'like', 'my', 'stuff', ',', 'rather', '.', 'That', "'s", 'why', 'I', 'go', 'on', '.', 'Prattle', ':', 'I', "'d", 'chuck', 'it', 'if', 'there', "'s", 'no', 'money', 'in', 'it', '.', 'de', 'Reves', ':', 'Ah', ',', 'but', 'then', 'it', "'s", 'hardly', 'in', 'your', 'line', ',', 'is', 'it', '?', 'You', "'d", 'hardly', 'approve', 'of', 'poetry', 'if', 'there', 'was', 'money', 'in', 'it', '.', 'Prattle', ':', 'Oh', ',', 'I', 'do', "n't", 'say', 'that', '.', 'If', 'I', 'could', 'make', 'as', 'much', 'by', 'poetry', 'as', 'I', 'can', 'by', 'betting', 'I', 'do', "n't", 'say', 'I', 'would', "n't", 'try', 'the', 'poetry', 'touch', ',', 'only', '-', '-', 'de', 'Reves', ':', 'Only', 'what', '?', 'Prattle', ':', 'Oh', ',', 'I', 'do', "n't", 'know', '.', 'Only', 'there', 'seems', 'more', 'sense', 'in', 'betting', ',', 'somehow', '.', 'de', 'Reves', ':', 'Well', ',', 'yes', '.', 'I', 'suppose', 'it', "'s", 'easier', 'to', 'tell', 'what', 'an', 'earthly', 'horse', 'is', 'going', 'to', 'do', ',', 'than', 'to', 'tell', 'what', 'Pegasus', '-', '-', 'Prattle', ':', 'What', "'s", 'Pegasus', '?', 'de', 'Reves', ':', 'Oh', ',', 'the', 'winged', 'horse', 'of', 'poets', '.', 'Prattle', ':', 'I', 'say', '!', 'You', 'do', "n't", 'believe', 'in', 'a', 'winged', 'horse', ',', 'do', 'you', '?', 'de', 'Reves', ':', 'In', 'our', 'trade', 'we', 'believe', 'in', 'all', 'fabulous', 'things', '.', 'They', 'all', 'represent', 'some', 'large', 'truth', 'to', 'us', '.', 'An', 'emblem', 'like', 'Pegasus', 'is', 'as', 'real', 'a', 'thing', 'to', 'a', 'poet', 'as', 'a', 'Derby', 'winner', 'would', 'be', 'to', 'you', '.', 'Prattle', ':', 'I', 'say', '.', '(', 'Give', 'me', 'a', 'cigarette', '.', 'Thanks', '.', ')', 'What', '?', 'Then', 'you', "'d", 'believe', 'in', 'nymphs', 'and', 'fauns', ',', 'and', 'Pan', ',', 'and', 'all', 'those', 'kind', 'of', 'birds', '?', 'de', 'Reves', ':', 'Yes', '.', 'Yes', '.', 'In', 
'all', 'of', 'them', '.', 'Prattle', ':', 'Good', 'Lord', '!', 'de', 'Reves', ':', 'You', 'believe', 'in', 'the', 'Lord', 'Mayor', 'of', 'London', ',', 'do', "n't", 'you', '?', 'Prattle', ':', 'Yes', ',', 'of', 'course', ';', 'but', 'what', 'has', 
'-', '-', 'de', 'Reves', ':', 'Four', 'million', 'people', 'or', 'so', 'made', 'him', 'Lord', 'Mayor', ',', 'did', "n't", 'they', '?', 'And', 'he', 'represents', 'to', 'them', 'the', 'wealth', 'and', 'dignity', 'and', 'tradition', 'of', '-', '-', 'Prattle', ':', 'Yes', ';', 'but', ',', 'I', 'say', ',', 'what', 'has', 'all', 'this', '-', '-', 'de', 'Reves', ':', 'Well', ',', 'he', 'stands', 'for', 'an', 'idea', 'to', 'them', ',', 'and', 'they', 'made', 'him', 'Lord', 'Mayor', ',', 'and', 'so', 'he', 'is', 'one', '...', 'Prattle', ':', 'Well', ',', 'of', 'course', 'he', 'is', '.', 'de', 'Reves', ':', 'In', 'the', 'same', 'way', 'Pan', 'has', 'been', 'made', 'what', 'he', 'is', 'by', 'millions', ';', 'by', 'millions', 'to', 'whom', 'he', 'represents', 'world-old', 'traditions', '.', 'Prattle', '(', 'rising', 'from', 'his', 'chair', 'and', 'stepping', 'backwards', ',', 'laughing', 'and', 'looking', 'at', 'the', 'Poet', 'in', 'a', 'kind', 'of', 'assumed', 'wonder', ')', ':', 'I', 'say', '...', 'I', 'say', '...', 'You', 'old', 'heathen', '...', 'but', 'Good', 'Lord', '...', '[', 'He', 'bumps', 'into', 'the', 'high', 'screen', 'behind', ',', 'pushing', 'it', 'back', 'a', 'little', '.', 'de', 'Reves', ':', 'Look', 'out', '!', 'Look', 'out', '!', 'Prattle', ':', 'What', '?', 'What', "'s", 'the', 'matter', '?', 'de', 'Reves', ':', 'The', 'screen', '!', 'Prattle', ':', 'Oh', ',', 'sorry', ',', 'yes', '.', 'I', "'ll", 'put', 'it', 'right', '.', '[', 'He', 'is', 'about', 'to', 'go', 'round', 'behind', 'it', '.', 'de', 'Reves', ':', 'No', ',', 'do', "n't", 'go', 'round', 'there', '.', 'Prattle', ':', 'What', '?', 'Why', 'not', '?', 'de', 'Reves', ':', 'Oh', ',', 'you', 'would', "n't", 'understand', '.', 'Prattle', ':', 'Would', "n't", 'understand', '?', 'Why', ',', 'what', 'have', 'you', 'got', '?', 'de', 'Reves', ':', 'Oh', ',', 'one', 'of', 'those', 'things', '...', 'You', 'would', "n't", 'understand', '.', 'Prattle', ':', 'Of', 'course', 'I', "'d", 'understand', '.', 'Let', "'s", 'have', 'a', 'look', '.', '[', 'The', 'Poet', 'walks', 'towards', 'Prattle', 'and', 'the', 'screen', '.', 'He', 'protests', 'no', 'further', '.', 'Prattle', 'looks', 'round', 'the', 'corner', 'of', 'the', 'screen', '.', 'An', 'altar', '.', 'de', 'Reves', '(', 'removing', 'the', 'screen', 'altogether', ')', ':', 'That', 'is', 'all', '.', 'What', 'do', 'you', 'make', 'of', 'it', '?', '[', 'An', 'altar', 'of', 'Greek', 'design', ',', 'shaped', 'like', 'a', 'pedestal', ',', 'is', 'revealed', '.', 'Papers', 'litter', 'the', 'floor', 'all', 'about', 'it', '.', 'Prattle', ':', 'I', 'say', '-', 'you', 'always', 'were', 'an', 'untidy', 'devil', '.', 'de', 'Reves', ':', 'Well', ',', 'what', 'do', 'you', 'make', 'of', 'it', '?', 'Prattle', ':', 'It', 'reminds', 'me', 'of', 'your', 'room', 'at', 'Eton', '.', 'de', 'Reves', ':', 'My', 'room', 'at', 'Eton', '?', 'Prattle', ':', 'Yes', ',', 'you', 'always', 'had', 'papers', 'all', 'over', 'your', 'floor', '.', 'de', 'Reves', ':', 'Oh', ',', 'yes', '-', '-', 'Prattle', ':', 'And', 'what', 'are', 'these', '?', 'de', 'Reves', ':', 'All', 'these', 'are', 'poems', ';', 'and', 'this', 'is', 'my', 'altar', 'to', 'Fame', '.', 'Prattle', ':', 'To', 'Fame', '?', 'de', 'Reves', ':', 'The', 'same', 'that', 'Homer', 'knew', '.', 'Prattle', ':', 'Good', 'Lord', '!', 'de', 'Reves', ':', 'Keats', 'never', 'saw', 'her', '.', 'Shelley', 'died', 'too', 'young', '.', 'She', 'came', 'late', 'at', 'the', 'best', 'of', 'times', ',', 'now', 'scarcely', 'ever', '.', 'Prattle', ':', 'But', ',', 'my', 'dear', 'fellow', ',', 'you', 'do', "n't", 'mean', 'that', 'you', 'think', 'there', 'really', 'is', 'such', 'a', 'person', '?', 'de', 'Reves', ':', 'I', 'offer', 'all', 'my', 'songs', 'to', 'her', '.', 'Prattle', ':', 'But', 'you', 'do', "n't", 'mean', 'you', 'think', 'you', 'could', 'actually', 'see', 'Fame', '?', 'de', 'Reves', ':', 'We', 'poets', 'personify', 'abstract', 'things', ',', 'and', 'not', 'poets', 'only', 'but', 'sculptors7', 'and', 'painters', 'too', '.', 'All', 'the', 'great', 'things', 'of', 'the', 'world', 'are', 'those', 'abstract', 'things', '.', 'Prattle', ':', 'But', 'what', 'I', 'mean', 'is', ',', 'they', "'re", 'not', 'really', 'there', ',', 'like', 'you', 'or', 'me', '.', 'de', 'Reves', ':', 'To', 'us', 'these', 'things', 'are', 'more', 'real', 'than', 'men', ',', 'they', 'outlive', 'generations', ',', 'they', 'watch', 'the', 'passing', 'of', 'kingdoms', ':', 'we', 'go', 'by', 'them', 'like', 'dust', ';', 'they', 'are', 'still', 'there', ',', 'unmoved', ',', 'unsmiling', '.', 'Prattle', ':', 'But', ',', 'but', ',', 'you', 'ca', "n't", 'think', 'that', 'you', 'could', 'see', 'Fame', ',', 'you', 'do', "n't", 'expect', 'to', 'see', 
'it', '?', 'de', 'Reves', ':', 'Not', 'to', 'me', '.', 'Never', 'to', 'me', '.', 'She', 'of', 'the', 'golden', 'trumpet', 'and', 'Greek', 'dress', 'will', 'never', 'appear', 'to', 'me', '...', 'We', 'all', 'have', 'our', 'dreams', '.', 'Prattle', ':', 'I', 'say', '-', 'what', 'have', 'you', 'been', 'doing', 'all', 'day', '?', 'de', 'Reves', ':', 'I', '?', 'Oh', ',', 'only', 'writing', 'a', 'sonnet', '.', 'Prattle', ':', 'Is', 'it', 'a', 'long', 'one', '?', 'de', 'Reves', ':', 'Not', 'very', 
'.', 'Prattle', ':', 'About', 'how', 'long', 'is', 'it', '?', 'de', 'Reves', ':', 'About', 'fourteen', 'lines', '.', 'Prattle', '(', 'impressively', ')', ':', 'I', 'tell', 'you', 'what', 'it', 'is', '.', 'de', 'Reves', ':', 'Yes', '?', 'Prattle', ':', 'I', 'tell', 'you', 'what', '.', 'You', "'ve", 'been', 'overworking', 'yourself', '.', 'I', 'once', 'got', 'like', 'that', 'on', 'board', 'the', 'Sandhurst', ',', 'working', 'for', 'the', 'passing-out', 'exam', '.', 'I', 'got', 'so', 'bad', 'that', 'I', 'could', 'have', 'seen', 'anything', '.', 'de', 'Reves', ':', 'Seen', 'anything', '?', 'Prattle', ':', 'Lord', ',', 'yes', ';', 'horned', 'pigs', ',', 'snakes', 'with', 'wings', ';', 'anything', ';', 'one', 'of', 'your', 'winged', 'horses', 'even', '.', 'They', 'gave', 'me', 'some', 'stuff', 'called', 'bromide', 'for', 'it', '.', 'You', 'take', 'a', 'rest', '.', 'de', 'Reves', ':', 'But', 'my', 'dear', 'fellow', ',', 'you', 'do', "n't", 'understand', 'at', 'all', '.', 'I', 'merely', 'said', 'that', 'abstract', 'things', 'are', 'to', 'a', 'poet', 'as', 'near', 'and', 'real', 'and', 'visible', 'as', 'one', 'of', 'your', 'bookmakers', 'or', 'barmaids', '.', 'Prattle', ':', 'I', 'know', '.', 'You', 'take', 'a', 'rest', '.', 'de', 'Reves', ':', 'Well', ',', 'perhaps', 'I', 'will', '.', 'I', "'d", 'come', 'with', 'you', 'to', 'that', 'musical', 'comedy', 'you', "'re", 'going', 'to', 'see', ',', 'only', 'I', "'m", 'a', 'bit', 'tired', 'after', 'writing', 'this', ';', 'it', "'s", 'a', 'tedious', 'job', '.', 'I', "'ll", 'come', 'another', 'night', '.', 'Prattle', ':', 'How', 'do', 'you', 'know', 'I', "'m", 'going', 'to', 'see', 'a', 'musical', 'comedy', '?', 'de', 'Reves', ':', 'Well', ',', 'where', 'would', 'you', 'go', '?', 'Hamlet', "'s", '8', 'on', 'at', 'the', 'Lord', 'Chamberlain', "'s", '.', 'You', "'re", 'not', 'going', 'there', '.', 'Prattle', ':', 'Do', 'I', 'look', 'like', 'it', '?', 'de', 'Reves', ':', 'No', '.', 'Prattle', ':', 'Well', ',', 'you', "'re", 'quite', 'right', '.', 'I', "'m", 'going', 'to', 'see', '``', 'The', 'Girl', 'from', 'Bedlam', '.', "''", 'So', 'long', '.', 'I', 'must', 'push', 'off', 'now', '.', 'It', "'s", 'getting', 'late', '.', 'You', 'take', 'a', 'rest', '.', 'Do', "n't", 'add', 'another', 'line', 'to', 'that', 'sonnet', ';', 'fourteen', "'s", 'quite', 'enough', '.', 'You', 'take', 'a', 'rest', '.', 'Do', "n't", 'have', 'any', 'dinner', 'to-night', ',', 'just', 'rest', '.', 'I', 'was', 'like', 'that', 'once', 'myself', '.', 'So', 'long', '.', 'de', 'Reves', ':', 'So', 'long', '.', '[', 'Exit', 'Prattle', '.', 'de', 'Reves', 'returns', 'to', 'his', 'table', 'and', 'sits', 'down', '.', 'Good', 'old', 'Dick', '!', 'He', "'s", 'the', 'same', 'as', 'ever', '.', 'Lord', ',', 'how', 'time', 'passes', '.', 'He', 'takes', 'his', 'pen', 'and', 'his', 'sonnet', 'and', 'makes', 'a', 'few', 'alterations', '.', 'Well', ',', 'that', "'s", 'finished', '.', 'I', 'ca', "n't", 'do', 'any', 'more', 'to', 'it', '.', '[', 'He', 'rises', 'and', 'goes', 'to', 'the', 'screen', ';', 'he', 'draws', 'back', 'part', 'of', 'it', 'and', 'goes', 'up', 'to', 'the', 'altar', '.', 'He', 'is', 'about', 'to', 'place', 'his', 'sonnet', 'reverently', 'at', 'the', 'foot', 'of', 'the', 'altar', 'amongst', 'his', 'other', 'verses', '.', 'No', ',', 'I', 'will', 'not', 'put', 'it', 'there', '.', 'This', 'one', 'is', 'worthy', 'of', 'the', 'altar', '.', '[', 'He', 'places', 'the', 'sonnet', 'upon', 'the', 'altar', 'itself', '.', 'If', 'that', 'sonnet', 'does', 'not', 'give', 'me', 'fame', ',', 'nothing', 'that', 'I', 'have', 'done', 'before', 'will', 'give', 'it', 'to', 'me', ',', 'nothing', 'that', 'I', 'ever', 'will', 'do', '.', '[', 'He', 'replaces', 'the', 'screen', 'and', 'returns', 'to', 'his', 'chair', 'at', 'the', 'table', '.', 'Twilight', 'is', 'coming', 'on', '.', 'He', 'sits', 'with', 'his', 'elbow', 'on', 'the', 'table', ',', 'his', 'head', 'on', 'his', 'hand', ',', 'or', 'however', 'the', 'actor', 'pleases', '.', 'Well', ',', 'well', '.', 'Fancy', 'seeing', 'Dick', 'again', '.', 'Well', ',', 'Dick', 'enjoys', 'his', 'life', ',', 'so', 'he', "'s", 'no', 'fool', '.', 'What', 'was', 'that', 'he', 'said', '?', '``', 'There', "'s", 'no', 'money', 'in', 'poetry', '.', 'You', "'d", 'better', 'chuck', 'it', '.', "''", 'Ten', 'years', "'", 'work', 'and', 'what', 'have', 'I', 'to', 'show', 'for', 'it', '?', 'The', 'admiration', 'of', 'men', 'who', 'care', 'for', 'poetry', ',', 'and', 'how', 'many', 'of', 'them', 'are', 'there', '?', 'There', "'s", 'a', 'bigger', 'demand', 'for', 'smoked', 'glasses', 'to', 'look', 'at', 'eclipses', 'of', 'the', 'sun', '.', 'Why', 'should', 'Fame', 'come', 'to', 'me', '?', 'Have', "n't", 'I', 'given', 'up', 'my', 'days', 'for', 'her', '?', 'That', 'is', 'enough', 'to', 'keep', 'her', 'away', '.', 'I', 'am', 'a', 'poet', ';', 'that', 'is', 'enough', 'reason', 'for', 'her', 'to', 'slight', 'me', '.', 'Proud', 'and', 'aloof', 'and', 'cold', 'as', 'marble', ',', 'what', 'does', 'Fame', 'care', 'for', 'us', '?', 'Yes', ',', 'Dick', 'is', 'right', '.', 'It', "'s", 'a', 'poor', 'game', 'chasing', 'illusions', ',', 'hunting', 'the', 'intangible', ',', 'pursuing', 'dreams', '.', 'Dreams', '?', 'Why', ',', 'we', 'are', 'ourselves', 'dreams', '.', '[', 'He', 'leans', 'back', 'in', 'his', 'chair', '.', 'We', 'are', 'such', 'stuff', 'As', 'dreams', 'are', 'made', 'on', ',', 'and', 'our', 'little', 'life', 'Is', 'rounded', 'with', 'a', 'sleep', '.', '[', 'He', 'is', 'silent', 'for', 'a', 'while', '.', 'Suddenly', 'he', 'lifts', 'his', 'head', '.', 'My', 'room', 'at', 'Eton', ',', 'Dick', 'said', '.', 'An', 'untidy', 'mess', '.', '[', 'As', 'he', 'lifts', 'his', 'head', 'and', 'says', 'these', 'words', ',', 'twilight', 'gives', 'place', 'to', 'broad', 'daylight', ',', 'merely', 'as', 'a', 'hint', 'that', 'the', 'author', 'of', 'the', 'play', 'may', 'have', 'been', 'mistaken', ',', 'and', 'the', 'whole', 'thing', 'may', 'have', 'been', 'no', 'more', 'than', 'a', 'poet', "'s", 'dream', '.', 'So', 'it', 'was', ',', 'and', 'it', "'s", 'an', 'untidy', 'mess', 'there', '(', 'looking', 'at', 'screen', ')', 'too', '.', 'Dick', "'s", 'right', '.', 'I', "'ll", 'tidy', 'it', 'up', '.', 'I', "'ll", 'burn', 'the', 'whole', 'damned', 'heap', ',', '[', 'He', 'advances', 'impetuously', 'towards', 'the', 'screen', '.', 'every', 'damned', 'poem', 'that', 'I', 'was', 'ever', 'fool', 'enough', 'to', 'waste', 'my', 'time', 'on', '.', '[', 'He', 'pushes', 'back', 'the', 'screen', '.', 'Fame', 'in', 'a', 'Greek', 'dress', 'with', 'a', 'long', 'golden', 'trumpet', 'in', 'her', 'hand', 'is', 'seen', 'standing', 'motionless', 'on', 'the', 'altar', 'like', 'a', 'marble', 'goddess', '.', 'So', '...', 'you', 'have', 'come', '!', '[', 'For', 'a', 'while', 'he', 'stands', 'thunderstruck', '.', 'Then', 'he', 'approaches', 'the', 'altar', '.', 'Divine', 'fair', 'lady', ',', 'you', 'have', 'come', '.', '[', 'He', 'holds', 'up', 'his', 'hand', 'to', 'her', 'and', 'leads', 'her', 'down', 'from', 'the', 'altar', 'and', 'into', 'the', 'centre', 'of', 'the', 'stage', '.', 'At', 'whatever', 'moment', 'the', 'actor', 'finds', 'it', 'most', 'convenient', ',', 'he', 'repossesses', 'himself', 'of', 
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12, 38, 12, 27, 38, 2, 6, 10, 38, 41, 5, 16, 30, 17, 10, 0, 30, 2, 11, 38, 10, 30, 10, 24, 6, 10, 38, 18, 5, 2, 10, 5, 2, 10, 5, 2, 10, 9, 26, 29, 29, 38, 0, 2, 6, 10, 9, 26, 29, 18, 7, 5, 2, 10, 15, 10, 38, 18, 16, 27, 38, 0, 16, 30, 2, 6, 10, 18, 41, 28, 5, 10, 42, 18, 38, 12, 15, 10, 38, 16, 9, 26, 16, 21, 38, 16, 9, 26, 2, 6, 6, 10, 38, 41, 16, 30, 18, 5, 2, 10, 38, 2, 6, 10, 5, 16, 27, 18, 6, 18, 24, 26, 17, 10, 21, 38, 41, 16, 30, 18, 2, 10, 38, 10, 5, 2, 6, 10, 5, 2, 6, 6, 10, 5, 17, 
10, 30, 29, 28, 6, 5, 2, 10, 5, 2, 10, 10, 38, 18, -1, 16, 31, 29, 22, 41, 5, 2, 5, 16, 30, 6, 38, 18, 16, 30, 2, 10, 38, 12, 6, 10, 38, 16, 31, 29, 38, 41, 16, 30, 21, 17, 10, 24, 16, 0, 30, 16, 21, 5, 2, 10, 0, 5, 2, 10, 5, 2, 10, 38, 5, 32, 10, 
2, 10, 30, 16, 20, 6, 38, 16, 30, 16, 5, 2, 10, 5, 16, 27, 29, 5, 2, 10, 38, 16, 18, 30, 16, 24, 12, 38, 2, 30, 17, 10, 38, 30, 16, 18, 29, 22, 41, 12, 30, 16, 0, 30, 16, 5, 10, 38, 5, 2, 12, 30, 16, 18, 38, 10, 38, 16, 31, 2, 10, 5, 2, 10, 38, 12, 12, 38, 33, 22, 10, 38, 2, 10, 38, 12, 12, 38, 16, 38, 16, 38, 18, -1, 26, 38, 10, 38, 16, 31, 16, 38, 12, 12, 38, 0, -1, 16, 30, 36, 6, -1, 31, 16, 16, 32, 27, 12, 22, 10, 38, 10, 22, 12, 38, 25, 38, 6, 6, 10, 38, 40, 12, 9, 36, 26, 2, 10, 38, 12, 12, 38, 12, 12, 22, 41, 12, 30, 18, 24, 2, 10, 38, 16, 30, 16, 0, 30, 17, 10, 21, 38, 12, 41, 5, 2, 10, 5, 32, 2, 10, 5, 2, 6, 10, 9, 26, 5, 10, 5, 2, 10, 27, 18, 6, 42, 38, 25, 22, 25, 22, 13, 22, 25, 22, 26, 38, 11, 22, 25, 22, 41, 2, 10, 5, 2, 10, 10, 30, 29, 38, 12, 30, 17, 10, 38, 12, 38, 25, 38, 16, 30, 2, 10, 22, 41, 18, 38, 5, 17, 10, 38, 42, 33, 30, 17, 10, 22, 12, 12, 38, 12, 12, 38, 10, 38, 16, 31, 30, 12, 12, 38, 12, 12, 38, 12, 12, 12, 38, 10, 38, 16, 30, 26, 16, 12, 38, 2, 10, 38, 11, 22, 11, 22, 11, 22, 10, 38, 26, 38, 33, 30, 17, 6, 10, 22, 12, 12, 38, 16, -1, 16, -1, 16, 31, 36, 18, 26, 38, 10, 38, 18, 38, 32, 31, 16, 5, 8, 38, 6, 0, 6, 22, 12, 12, 38, 25, 38, 25, 38, 6, 38, 41, 16, 30, 17, 10, 21, 5, 2, 10, 38, 25, 38, 25, 38, 16, 31, 6, 38, 10, 38, 12, 30, 17, 6, 10, 38, 2, 10, 38, 11, 22, 11, 22, 11, 22, 12, 38, 39, 6, 38, 26, 16, 10, 38, 16, 31, 24, 26, 2, 18, 6, 38, 12, 12, 38, 9, 36, 1, 16, 18, -1, 9, 16, 26, 24, 26, 17, 10, 38, 5, 16, 9, 38, 25, -1, 12, 41, 28, 21, 10, 42, 38, 18, 38, 33, 30, 2, 22, 12, 12, 38, 25, 38, 32, 30, 17, 10, 38, 12, 41, 5, 2, 10, 5, 16, 31, 42, 38, 16, 30, 5, 2, 10, 38, 41, 12, 5, 2, 10, 38, 12, 41, 28, 24, 2, 10, 42, 38, 18, 38, 33, 31, 16, 29, 5, 18, 22, 12, 12, 38, 
25, -1, 25, -1, 2, 31, 17, 10, 11, 38, 12, 41, 28, 2, 6, 10, 42, 38, 33, 31, 18, 29, 5, 2, 1, 22, 12, 12, 41, 18, 42, 38, 25, 38, 11, 0, 10, 38, 12, 41, 5, 2, 10, 42, 38, 16, 30, 11, 0, 10, 5, 10, 38, 2, 10, 38, 6, 10, 10, 38, 11, 22, 6, 6, 10, 38, 11, 22, 6, 6, 10, 38, 11, 22, 12, 38, 25, 38, 0, 33, 30, 2, 22, 12, 12, 41, 18, 42, 38, 25, 38, 2, 10, 10, 38, 10, 38, 16, 30, 2, 10, 15, 10, 22, 16, 30, 2, 6, 10, 22, 16, 30, 2, 6, 10, 22, 41, 12, 11, 5, 2, 12, 38, 2, 10, 18, 5, 11, 15, 11, 38, 12, 12, 38, 25, 38, 2, 30, 6, 38, 2, 30, 6, 38, 2, 30, 6, 38, 41, 12, 30, 2, 10, 5, 17, 10, 38, 16, 30, 18, 24, 26, 38, 12, 12, 41, 18, 0, 18, 42, 38, 1, 10, 38, 1, 10, -1, 10, 38, 18, 38, 18, 5, 16, 38, 12, 12, 38, 5, 1, 11, 38, 6, 10, 38, 16, 31, 
29, 16, 38, 28, 14, 17, 11, -1, 16, 31, -1, 16, 31, 16, 31, 36, 6, -1, 12, 38, 25, 38, 16, 31, 2, 10, 38, 12, 12, 38, 25, 38, 25, 38, 16, 31, 36, 6, 38, 16, 9, 36, 26, 38, 16, 31, 36, 18, 26, 38, 11, 31, 16, 7, 38, 16, 9, 26, 16, 22, 16, 31, 36, 18, 26, 16, 38, 11, 31, 6, 38, 16, 9, 26, 11, 38, 0, 16, 38, 25, 38, 25, 38, 25, 38, 16, 9, 36, 26, 38, 16, 9, 36, 26, 38, 25, 38, 26, 16, 38, 0, 16, 9, 36, 38, 41, 18, 12, 30, 29, 28, 1, 5, 17, 11, 38, 16, 30, 5, 2, 6, 10, 38, 30, 18, 18, 38, 0, 30, 17, 11, 18, 18, 5, 2, 10, 5, 2, 10, 15, 11, 38, 25, 38, 16, 31, 16, 26, 16, 38, 0, 38, 16, 9, 36, 26, 38, 12, 38, 25, 38, 32, 30, 18, 6, 38, 6, 10, 38, 2, 10, 38, 16, 31, 36, 28, 24, 26, 16, 38, 12, 12, 38, 0, 38, 18, 38, 18, 38, 16, 31, 36, 26, 38, 10, 38, 16, 31, 29, 24, 26, 38, 16, 31, 38, 41, 16, 30, 2, 10, 5, 10, 5, 17, 10, 38, 10, 38], 'genre': 'Drama', 'subgenre': 'drama', 'year': '1919', 'quarter_cent': '1900-1924', 'decade': '1910s', 'title': 'Fame and the poet', 'author': 'Dunsany [Edward John Moreton Drax Plunkett]', 'notes': '', 'comments': 'selected from larger file', 'period': '1850-1920', 'id': '317'}
```

### Data Fields

There are three configs in this dataset- `plain`, `class` and `pos`. `plain` is a simple text dataset whereas `pos` and `class` are both annotated datasets containing pos tagging. A `plain` data point has the following fields:
```
             {
                    "text": The text in the sample("string"),
                    "genre": The genre of the text("string"),
                    "subgenre": The subgenre of the text("string"),
                    "year": The year the text was produced("string"),
                    "quarter_cent": The quarter century in which the text was produced("string"),
                    "decade": The decade the text was produced("string"),
                    "title": The title of the text("string"),
                    "author": The author of the text("string"),
                    "notes": Notes about the text, if any("string"),
                    "comments": Commentsabout the text, if any("string"),
                    "period": 70-year period during which the text was produced("string"),
                    "id": Unqiue identifier("string"),
                }
```

A typical `pos`/`class` data point has the following fields:

```
                {
                    "text": The tokens in the sample(list("string")),
                    "pos_tags": Corresponding POS tags for the tokens (list("string"))
                    "genre": The genre of the text("string"),
                    "subgenre": The subgenre of the text("string"),
                    "year": The year the text was produced("string"),
                    "quarter_cent": The quarter century in which the text was produced("string"),
                    "decade": The decade the text was produced("string"),
                    "title": The title of the text("string"),
                    "author": The author of the text("string"),
                    "notes": Notes about the text, if any("string"),
                    "comments": Commentsabout the text, if any("string"),
                    "period": 70-year period during which the text was produced("string"),
                    "id": Unqiue identifier("string"),
                }
```

### Data Splits

Train: 333

## Dataset Creation

### Curation Rationale

The Corpus of Late Modern English Texts (CLMET) is a corpus of roughly 35 million words of
British English from 17101920, grouped into three 70-year periods (De Smet 2005; Diller et
al. 2011). The history, versions and specifics of corpus composition can be followed up by
referring to the CLMET3.0 website. CLMET3.0 is currently distributed in three formats: (i)
plain text, (ii) plain text with one sentence per line, and (iii) a tagged version (one sentence
per line).
Version CLMET3.1 is the result of making CLMET available in a CQP format for use in
CWB and CQPweb-based corpus environments (Evert & Hardie 2011; Evert 2010a). While
there is no change to the selection of texts, CLMET3.1 includes additions and changes in
linguistic annotation. The changes in CLMET3.1 are of three general types: (a) retokenization
and retagging, (b) fixing of some systematic issues that come with historical data, and (c)
enhancing annotation by adding lemmas and simplified part-of-speech class tags

### Source Data

#### Initial Data Collection and Normalization

The initial data is from OCR of texts in English from 1710-1920

#### Who are the source language producers?

The text was produced by the authors of the original work and then OCRd

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

This dataset does not contain any personal information as these are historic texts. Some content might be sensitive

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

Dealing with historical data, tagging remains problematic in all areas, and should be treated
with caution (especially with noun recognition) and/or combined with more coarse-grained
class queries. Also bear in mind that the lemmas for unknown items are in lower
case, while proper names that the tagger did recognize are not necessarily all lower case. In
addition, lemmatization may not be consistent, e.g. in the area of -ize/ise spellings; these were
not homogenized to preserve as much of the original orthography as possible.

## Additional Information

### Dataset Curators

The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�

### Licensing Information

Creative Commons Attribution Non Commercial Share Alike 4.0 International

### Citation Information

[Needs More Information]
task_categories:text-classification task_categories:fill-mask task_ids:multi-label-classification task_ids:masked-language-modeling annotations_creators:expert-generated annotations_creators:machine-generated language_creators:found multilinguality:monolingual size_categories:n<1K source_datasets:original language:en license:cc-by-sa-4.0 region:us
{"annotations_creators": ["expert-generated", "machine-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification", "fill-mask"], "task_ids": ["multi-label-classification", "masked-language-modeling"], "pretty_name": "Corpus of Late Modern English Texts v3.1"}
TAGS
#task_categories-text-classification #task_categories-fill-mask #task_ids-multi-label-classification #task_ids-masked-language-modeling #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us 

Dataset Card for clmet\_3\_1
============================


NOTES:


* Some of the annotations in the 'class' and 'pos' configs are not properly formed. These are indicated with warning messages when the dataset is loaded.
* In addition to the classes mentioned in the README for the dataset, there is an additional class in the 'class' dataset called 'QUOT'. As far as I can tell, this is used for tagging all quotation marks
* When the 'class' and 'pos' configs are loaded, the available class/pos tags are shown at the top


Dataset Statistics:
-------------------


The following table summarises the corpus make-up:





```
                   | | 1710-1780|  1780-1850 | 1850-1920 |

```

|Narrative fiction | 5,405,645 | 5,780,352 | 7,561,339 |
|Narrative non-fiction | 2,145,946 | 2,261,485 | 1,097,487 |
|Drama | 523,318 | 441,040 | 763,352 |
|Letters | 1,208,219 | 842,795 | 554,046 |
|Treatise | 1,263,090 | 1,927,272 | 2,030,210 |
|Other | 1,635,846 | 2,047,513 | 2,851,805 |


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information


Dataset Description
-------------------


* Homepage: URL
* Repository:
* Paper: URL
* Leaderboard:
* Point of Contact: Henrik De Smet


### Dataset Summary


The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�, as an offshoot of a bigger project developing a database of text descriptors (Diller, De Smet & Tyrkk� 2011). CLMET3.1 is a principled collection of public domain texts drawn from various online archiving projects. In total, the corpus contains some 34 million words of running text. It incorporates CLMET, CLMETEV, and CLMET3.0, and has been compiled following roughly the same principles, that is:


* The corpus covers the period 17101920, divided into three 70-year sub-periods.
* The texts making up the corpus have all been written by British and Irish authors who are native speakers of English.
* The corpus never contains more than three texts by the same author.
* The texts within each sub-period have been written by authors born within a correspondingly restricted sub-period.


### Supported Tasks and Leaderboards


* 'named-entity-recognition': Since this dataset is tagged, it can be used for performing NER
* 'text-classification': Each text comes with the date of the text and can be used to perform stylistic classification of texts


### Languages


The text in the dataset is in English. The associated BCP-47 code is 'en'


Dataset Structure
-----------------


### Data Instances


A 'plain' sample looks as follows:


A 'pos' sample looks as follows:


### Data Fields


There are three configs in this dataset- 'plain', 'class' and 'pos'. 'plain' is a simple text dataset whereas 'pos' and 'class' are both annotated datasets containing pos tagging. A 'plain' data point has the following fields:


A typical 'pos'/'class' data point has the following fields:


### Data Splits


Train: 333


Dataset Creation
----------------


### Curation Rationale


The Corpus of Late Modern English Texts (CLMET) is a corpus of roughly 35 million words of
British English from 17101920, grouped into three 70-year periods (De Smet 2005; Diller et
al. 2011). The history, versions and specifics of corpus composition can be followed up by
referring to the CLMET3.0 website. CLMET3.0 is currently distributed in three formats: (i)
plain text, (ii) plain text with one sentence per line, and (iii) a tagged version (one sentence
per line).
Version CLMET3.1 is the result of making CLMET available in a CQP format for use in
CWB and CQPweb-based corpus environments (Evert & Hardie 2011; Evert 2010a). While
there is no change to the selection of texts, CLMET3.1 includes additions and changes in
linguistic annotation. The changes in CLMET3.1 are of three general types: (a) retokenization
and retagging, (b) fixing of some systematic issues that come with historical data, and (c)
enhancing annotation by adding lemmas and simplified part-of-speech class tags


### Source Data


#### Initial Data Collection and Normalization


The initial data is from OCR of texts in English from 1710-1920


#### Who are the source language producers?


The text was produced by the authors of the original work and then OCRd


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


This dataset does not contain any personal information as these are historic texts. Some content might be sensitive


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Dealing with historical data, tagging remains problematic in all areas, and should be treated
with caution (especially with noun recognition) and/or combined with more coarse-grained
class queries. Also bear in mind that the lemmas for unknown items are in lower
case, while proper names that the tagger did recognize are not necessarily all lower case. In
addition, lemmatization may not be consistent, e.g. in the area of -ize/ise spellings; these were
not homogenized to preserve as much of the original orthography as possible.


Additional Information
----------------------


### Dataset Curators


The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�


### Licensing Information


Creative Commons Attribution Non Commercial Share Alike 4.0 International



### Dataset Summary


The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�, as an offshoot of a bigger project developing a database of text descriptors (Diller, De Smet & Tyrkk� 2011). CLMET3.1 is a principled collection of public domain texts drawn from various online archiving projects. In total, the corpus contains some 34 million words of running text. It incorporates CLMET, CLMETEV, and CLMET3.0, and has been compiled following roughly the same principles, that is:


* The corpus covers the period 17101920, divided into three 70-year sub-periods.
* The texts making up the corpus have all been written by British and Irish authors who are native speakers of English.
* The corpus never contains more than three texts by the same author.
* The texts within each sub-period have been written by authors born within a correspondingly restricted sub-period. ### Supported Tasks and Leaderboards


* 'named-entity-recognition': Since this dataset is tagged, it can be used for performing NER
* 'text-classification': Each text comes with the date of the text and can be used to perform stylistic classification of texts ### Languages


The text in the dataset is in English. The associated BCP-47 code is 'en'


Dataset Structure
----------------- ### Data Instances


A 'plain' sample looks as follows:


A 'pos' sample looks as follows: ### Data Fields


There are three configs in this dataset- 'plain', 'class' and 'pos'. 'plain' is a simple text dataset whereas 'pos' and 'class' are both annotated datasets containing pos tagging. A 'plain' data point has the following fields:


A typical 'pos'/'class' data point has the following fields: ### Data Splits


Train: 333


Dataset Creation
---------------- ### Curation Rationale


The Corpus of Late Modern English Texts (CLMET) is a corpus of roughly 35 million words of
British English from 17101920, grouped into three 70-year periods (De Smet 2005; Diller et
al. 2011). The history, versions and specifics of corpus composition can be followed up by
referring to the CLMET3.0 website. CLMET3.0 is currently distributed in three formats: (i)
plain text, (ii) plain text with one sentence per line, and (iii) a tagged version (one sentence
per line).
Version CLMET3.1 is the result of making CLMET available in a CQP format for use in
CWB and CQPweb-based corpus environments (Evert & Hardie 2011; Evert 2010a). While
there is no change to the selection of texts, CLMET3.1 includes additions and changes in
linguistic annotation. The changes in CLMET3.1 are of three general types: (a) retokenization
and retagging, (b) fixing of some systematic issues that come with historical data, and (c)
enhancing annotation by adding lemmas and simplified part-of-speech class tags ### Source Data #### Initial Data Collection and Normalization


The initial data is from OCR of texts in English from 1710-1920 #### Who are the source language producers?


The text was produced by the authors of the original work and then OCRd ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


This dataset does not contain any personal information as these are historic texts. Some content might be sensitive


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Dealing with historical data, tagging remains problematic in all areas, and should be treated
with caution (especially with noun recognition) and/or combined with more coarse-grained
class queries. Also bear in mind that the lemmas for unknown items are in lower
case, while proper names that the tagger did recognize are not necessarily all lower case. In
addition, lemmatization may not be consistent, e.g. in the area of -ize/ise spellings; these were
not homogenized to preserve as much of the original orthography as possible.


Additional Information
---------------------- ### Dataset Curators


The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk� ### Licensing Information


Creative Commons Attribution Non Commercial Share Alike 4.0 International
TAGS
#task_categories-text-classification #task_categories-fill-mask #task_ids-multi-label-classification #task_ids-masked-language-modeling #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us 
 ### Dataset Summary


The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�, as an offshoot of a bigger project developing a database of text descriptors (Diller, De Smet & Tyrkk� 2011). CLMET3.1 is a principled collection of public domain texts drawn from various online archiving projects. In total, the corpus contains some 34 million words of running text. It incorporates CLMET, CLMETEV, and CLMET3.0, and has been compiled following roughly the same principles, that is:


* The corpus covers the period 17101920, divided into three 70-year sub-periods.
* The texts making up the corpus have all been written by British and Irish authors who are native speakers of English.
* The corpus never contains more than three texts by the same author.
* The texts within each sub-period have been written by authors born within a correspondingly restricted sub-period. ### Supported Tasks and Leaderboards


* 'named-entity-recognition': Since this dataset is tagged, it can be used for performing NER
* 'text-classification': Each text comes with the date of the text and can be used to perform stylistic classification of texts ### Languages


The text in the dataset is in English. The associated BCP-47 code is 'en'


Dataset Structure
----------------- ### Data Instances


A 'plain' sample looks as follows:


A 'pos' sample looks as follows: ### Data Fields


There are three configs in this dataset- 'plain', 'class' and 'pos'. 'plain' is a simple text dataset whereas 'pos' and 'class' are both annotated datasets containing pos tagging. A 'plain' data point has the following fields:


A typical 'pos'/'class' data point has the following fields: ### Data Splits


Train: 333


Dataset Creation
---------------- ### Curation Rationale


The Corpus of Late Modern English Texts (CLMET) is a corpus of roughly 35 million words of
British English from 17101920, grouped into three 70-year periods (De Smet 2005; Diller et
al. 2011). The history, versions and specifics of corpus composition can be followed up by
referring to the CLMET3.0 website. CLMET3.0 is currently distributed in three formats: (i)
plain text, (ii) plain text with one sentence per line, and (iii) a tagged version (one sentence
per line).
Version CLMET3.1 is the result of making CLMET available in a CQP format for use in
CWB and CQPweb-based corpus environments (Evert & Hardie 2011; Evert 2010a). While
there is no change to the selection of texts, CLMET3.1 includes additions and changes in
linguistic annotation. The changes in CLMET3.1 are of three general types: (a) retokenization
and retagging, (b) fixing of some systematic issues that come with historical data, and (c)
enhancing annotation by adding lemmas and simplified part-of-speech class tags ### Source Data #### Initial Data Collection and Normalization


The initial data is from OCR of texts in English from 1710-1920 #### Who are the source language producers?


The text was produced by the authors of the original work and then OCRd ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


This dataset does not contain any personal information as these are historic texts. Some content might be sensitive


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Dealing with historical data, tagging remains problematic in all areas, and should be treated
with caution (especially with noun recognition) and/or combined with more coarse-grained
class queries. Also bear in mind that the lemmas for unknown items are in lower
case, while proper names that the tagger did recognize are not necessarily all lower case. In
addition, lemmatization may not be consistent, e.g. in the area of -ize/ise spellings; these were
not homogenized to preserve as much of the original orthography as possible.


Additional Information
---------------------- ### Dataset Curators


The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk� ### Licensing Information


Creative Commons Attribution Non Commercial Share Alike 4.0 International
passage: TAGS
#task_categories-text-classification #task_categories-fill-mask #task_ids-multi-label-classification #task_ids-masked-language-modeling #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us 
### Dataset Summary


The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�, as an offshoot of a bigger project developing a database of text descriptors (Diller, De Smet & Tyrkk� 2011). CLMET3.1 is a principled collection of public domain texts drawn from various online archiving projects. In total, the corpus contains some 34 million words of running text. It incorporates CLMET, CLMETEV, and CLMET3.0, and has been compiled following roughly the same principles, that is:


* The corpus covers the period 17101920, divided into three 70-year sub-periods.
* The texts making up the corpus have all been written by British and Irish authors who are native speakers of English.
* The corpus never contains more than three texts by the same author.
* The texts within each sub-period have been written by authors born within a correspondingly restricted sub-period.### Supported Tasks and Leaderboards


* 'named-entity-recognition': Since this dataset is tagged, it can be used for performing NER
* 'text-classification': Each text comes with the date of the text and can be used to perform stylistic classification of texts### Languages


The text in the dataset is in English. The associated BCP-47 code is 'en'


Dataset Structure
-----------------### Data Instances


A 'plain' sample looks as follows:


A 'pos' sample looks as follows: passage: ### Data Fields


There are three configs in this dataset- 'plain', 'class' and 'pos'. 'plain' is a simple text dataset whereas 'pos' and 'class' are both annotated datasets containing pos tagging. A 'plain' data point has the following fields:


A typical 'pos'/'class' data point has the following fields:### Data Splits


Train: 333


Dataset Creation
----------------### Curation Rationale


The Corpus of Late Modern English Texts (CLMET) is a corpus of roughly 35 million words of
British English from 17101920, grouped into three 70-year periods (De Smet 2005; Diller et
al. 2011). The history, versions and specifics of corpus composition can be followed up by
referring to the CLMET3.0 website. CLMET3.0 is currently distributed in three formats: (i)
plain text, (ii) plain text with one sentence per line, and (iii) a tagged version (one sentence
per line).
Version CLMET3.1 is the result of making CLMET available in a CQP format for use in
CWB and CQPweb-based corpus environments (Evert & Hardie 2011; Evert 2010a). While
there is no change to the selection of texts, CLMET3.1 includes additions and changes in
linguistic annotation. The changes in CLMET3.1 are of three general types: (a) retokenization
and retagging, (b) fixing of some systematic issues that come with historical data, and (c)
enhancing annotation by adding lemmas and simplified part-of-speech class tags### Source Data#### Initial Data Collection and Normalization


The initial data is from OCR of texts in English from 1710-1920#### Who are the source language producers?


The text was produced by the authors of the original work and then OCRd### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information


This dataset does not contain any personal information as these are historic texts. Some content might be sensitive


Considerations for Using the Data
---------------------------------### Social Impact of Dataset### Discussion of Biases