| Name | ProQuo JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | None |
| Summary | ProQuo is a tool for the detection of short quotations (<= 4 words) between two texts, a source text and a target text. The target text is the text quoting the source text. Quotations in the target text need to be clearly marked with quotations marks. |
| upload_time | 2024-04-16 08:27:44 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
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| keywords |
quotation detection
quotation identification
literal citation extraction
natural language processing
nlp
text reuse
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
# Readme
This repository contains two tools, `ProQuo` and `ProQuoLM`. Both are tools for the detection of short quotations
(<= 4 words) between two texts, a source text and a target text. The target text is the text quoting the source text.
Quotations in the target text need to be marked with quotations marks. For more information, see below.
The main purpose of this tool is to use the pretrained models for the detection of short quotations. While we found both
approaches (`ProQuo` and `ProQuoLM`) to perform at the same level (for details, see our [publication](https://jcls.io/article/id/3590/)),
`ProQuoLm` is easier to use, better maintained and the **recommended approach**.
## Quotation Marks
By default, the "best", that is, most common, combination of opening and closing quotation mark in the specific text is used.
The following combinations are automatically tried:
1. " and "
2. „ and “
3. „ and "
4. “ and “
5. » and «
6. « and »
7. ‘ and ’
If this is not the desired behaviour, quotations marks can be manually defined using the command line options
`--open-quote` and `--close-quote`.
## Approaches Overview
`ProQuo` is a specialized pipeline which uses a [model for reference classification](proquo/model/reference/ReferenceModelTrainer.py)
and a [model for relation extraction](proquo/model/relation/RelationModelBertTrainer.py) between quotations and (page)
references to distinguish between relevant quotations (that is, quotations from the source text) and quotations
from other sources. In a third step, a rule-based algorithm is used to link the identified quotations to their source.
`ProQuoLM` uses a [fine-tuned BERT model](https://huggingface.co/Fredr0id/proquolm) in two ways: to distinguish between
relevant quotations and quotations from other sources and to link the quotations to their source.
## Pretrained Models and Training Data
The pretrained models and training data are made available and can be downloaded from [here](https://scm.cms.hu-berlin.de/schluesselstellen/proquodata).
For `ProQuoLm`, we also provide a model on [Hugging Face](https://huggingface.co/Fredr0id/proquolm). This is used by default.
## Installation
### From PyPi
**Note**: Both tools are part of the same PyPi package. So the following command installs both.
~~~
pip install ProQuo
~~~
### From Source
Checkout this repository and then run:
~~~
python -m pip install .
~~~
### Dependencies
Both installation methods install all dependencies except `tensorflow` which needs to be installed manually depending on
the individual needs, see [Tensorflow installation](https://www.tensorflow.org/install). The latest version that was tested is 2.14.1.
For `RelationModelLstmTrainer`, `tensorflow-text` is needed. `RelationModelLstmTrainer` should normally not be needed as
`RelationModelBertTrainer` performs better and is the default in the pipeline.
## Usage
The following sections describe how to use ProQuo on the command line.
### Quotation detection
To run `ProQuoLM` with the default model, use the following command:
~~~
proquolm compare path_to_source_text path_to_target_text --text --output-type text
~~~
<details>
<summary>All ProQuoLM command line options</summary>
~~~
usage: proquolm compare [-h] [--tokenizer TOKENIZER] [--model MODEL]
[--lower-case | --no-lower-case]
[--output-folder-path OUTPUT_FOLDER_PATH]
[--create-dated-subfolder | --no-create-dated-subfolder]
[--text | --no-text] [--output-type {json,text,csv}]
[--csv-sep CSV_SEP] [--open-quote OPEN_QUOTE]
[--close-quote CLOSE_QUOTE]
[--include-long-matches-in-result]
[--max-num-processes MAX_NUM_PROCESSES]
source-file-path target-path
ProQuoLm compare allows the user to find short quotations (<= 4 words) in two
texts, a source text and a target text. The target text is the text quoting
the source text. Quotations in the target text need to be clearly marked with
quotations marks.
positional arguments:
source-file-path Path to the source text file
target-path Path to the target text file or folder
options:
-h, --help show this help message and exit
--tokenizer TOKENIZER
Name of the tokenizer to load from Hugging Face or
path to the tokenizer folder
--model MODEL Name of the model to load from Hugging Face or path to
the model folder
--lower-case, --no-lower-case
Run model inference on lower case text (default: True)
--output-folder-path OUTPUT_FOLDER_PATH
The output folder path. If this option is set the
output will be saved to a file created in the
specified folder
--create-dated-subfolder, --no-create-dated-subfolder
Create a subfolder named with the current date to
store the results (default: False)
--text, --no-text Include matched text in the returned data structure
(default: True)
--output-type {json,text,csv}
The output type
--csv-sep CSV_SEP output separator for csv (default: '\t')
--open-quote OPEN_QUOTE
The quotation open character. If this option is not
set, then the type of quotation marks used in a target
text is auto automatically identified.
--close-quote CLOSE_QUOTE
The quotation close character. If this option is not
set, then the type of quotation marks used in a target
text is auto automatically identified.
--include-long-matches-in-result
Include matches longer than 4 words in the output
--max-num-processes MAX_NUM_PROCESSES
Maximum number of processes to use for parallel
processing. This can significantly speed up the
process.
~~~
</details>
To run `ProQuo`, use the following command:
~~~
proquo compare path_to_source_text path_to_target_text
path_to_the_reference_vocab_file
path_to_the_reference_model_file
path_to_the_relation_tokenizer_folder
path_to_the_relation_model_folder
--text
--output-type text
~~~
`--output-type text` prints the results to the command line. To save the results to a file, use `--output-type csv` or
`--output-type json`. `--text` includes the quotation text in the output.
The output will look something like this:
~~~
10 15 500 505 quote quote
1000 1016 20 36 some other quote some other quote
~~~
The first two numbers are the character start and end positions in the source text and the other two numbers are the
character start and end positions in the target text.
<details>
<summary>All ProQuo command line options</summary>
~~~
usage: proquo compare [-h] [--quid-match-path QUID_MATCH_PATH]
[--output-folder-path OUTPUT_FOLDER_PATH]
[--create-dated-subfolder] [--no-create-dated-subfolder]
[--parallel-print-files [PARALLEL_PRINT_FILES ...]]
[--parallel-print-first-page PARALLEL_PRINT_FIRST_PAGE]
[--parallel-print-last-page PARALLEL_PRINT_LAST_PAGE]
[--text] [--no-text] [--ref] [--no-ref]
[--output-type {json,text,csv}] [--csv-sep CSV_SEP]
[--open-quote OPEN_QUOTE] [--close-quote CLOSE_QUOTE]
[--include-long-matches-in-result]
[--max-num-processes MAX_NUM_PROCESSES]
source-file-path target-path ref-vocab-file-path
ref-model-file-path rel-tokenizer-folder-path
rel-model-folder-path
ProQuo compare allows the user to find short quotations (<= 4 words) in two
texts, a source text and a target text. The target text is the text quoting
the source text. Quotations in the target text need to be clearly marked with
quotations marks.
positional arguments:
source-file-path Path to the source text file
target-path Path to the target text file or folder
ref-vocab-file-path Path to the reference vocab text file
ref-model-file-path Path to the reference model file
rel-tokenizer-folder-path
Path to the relation tokenizer folder
rel-model-folder-path
Path to the relation model folder
options:
-h, --help show this help message and exit
--quid-match-path QUID_MATCH_PATH
Path to the file or folder with quid matches. If this
option is not set, then Quid is used to find long
matches.
--output-folder-path OUTPUT_FOLDER_PATH
The output folder path. If this option is set the
output will be saved to a file created in the
specified folder
--create-dated-subfolder
Create a subfolder named with the current date to
store the results
--no-create-dated-subfolder
Do not create a subfolder named with the current date
to store the results
--parallel-print-files [PARALLEL_PRINT_FILES ...]
Filenames of files which quote a parallel print
edition
--parallel-print-first-page PARALLEL_PRINT_FIRST_PAGE
Number of the first page with parallel print
--parallel-print-last-page PARALLEL_PRINT_LAST_PAGE
Number of the last page with parallel print
--text Include matched text in the returned data structure
--no-text Do not include matched text in the returned data
structure
--ref Include matched reference in the returned data
structure
--no-ref Do not include matched reference in the returned data
structure
--output-type {json,text,csv}
The output type
--csv-sep CSV_SEP output separator for csv (default: '\t')
--open-quote OPEN_QUOTE
The quotation open character. If this option is not
set, then the type of quotation marks used in a target
text is auto automatically identified.
--close-quote CLOSE_QUOTE
The quotation close character. If this option is not
set, then the type of quotation marks used in a target
text is auto automatically identified.
--include-long-matches-in-result
Include matches longer than 4 words in the output
--max-num-processes MAX_NUM_PROCESSES
Maximum number of processes to use for parallel
processing.This can significantly speed up the
process.
~~~
</details>
## Parallel processing
`ProQuo` and `ProQuoLM` use [Quid](https://scm.cms.hu-berlin.de/schluesselstellen/quid) in the background which supports
using multiple processes when comparing multiple target texts with the source texts. To use Quid with multiple processes
the command line option `--max-num-processes` is used. The default is 1.
## Training
The library also supports training and testing of custom models. The [Training Readme](Training-Readme.md) gives an introduction to
training models.
## Citation
If you use `ProQuo` or `ProQuoLM` or base your work on our code, please cite our paper:
~~~
@article{arnold2023,
author = {Frederik Arnold, Robert Jäschke},
title = {A Novel Approach for Identification and Linking of Short Quotations in Scholarly Texts and Literary Works},
volume = {2},
year = {2023},
url = {https://jcls.io/article/id/3590/},
issue = {1},
doi = {10.48694/jcls.3590},
month = {1},
publisher={Universitäts- und Landesbibliothek Darmstadt},
journal = {Journal of Computational Literary Studies}
}
~~~
Raw data
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"author_email": "Frederik Arnold <frederik.arnold@hu-berlin.de>",
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"description": "# Readme\nThis repository contains two tools, `ProQuo` and `ProQuoLM`. Both are tools for the detection of short quotations\n(<= 4 words) between two texts, a source text and a target text. The target text is the text quoting the source text.\nQuotations in the target text need to be marked with quotations marks. For more information, see below.\n\nThe main purpose of this tool is to use the pretrained models for the detection of short quotations. While we found both\napproaches (`ProQuo` and `ProQuoLM`) to perform at the same level (for details, see our [publication](https://jcls.io/article/id/3590/)),\n`ProQuoLm` is easier to use, better maintained and the **recommended approach**.\n\n## Quotation Marks\nBy default, the \"best\", that is, most common, combination of opening and closing quotation mark in the specific text is used.\nThe following combinations are automatically tried:\n\n1. \" and \"\n2. \u201e and \u201c\n3. \u201e and \"\n4. \u201c and \u201c\n5. \u00bb and \u00ab\n6. \u00ab and \u00bb\n7. \u2018 and \u2019\n\nIf this is not the desired behaviour, quotations marks can be manually defined using the command line options\n`--open-quote` and `--close-quote`.\n\n## Approaches Overview\n`ProQuo` is a specialized pipeline which uses a [model for reference classification](proquo/model/reference/ReferenceModelTrainer.py)\nand a [model for relation extraction](proquo/model/relation/RelationModelBertTrainer.py) between quotations and (page)\nreferences to distinguish between relevant quotations (that is, quotations from the source text) and quotations\nfrom other sources. In a third step, a rule-based algorithm is used to link the identified quotations to their source.\n\n`ProQuoLM` uses a [fine-tuned BERT model](https://huggingface.co/Fredr0id/proquolm) in two ways: to distinguish between\nrelevant quotations and quotations from other sources and to link the quotations to their source.\n\n## Pretrained Models and Training Data\nThe pretrained models and training data are made available and can be downloaded from [here](https://scm.cms.hu-berlin.de/schluesselstellen/proquodata).\nFor `ProQuoLm`, we also provide a model on [Hugging Face](https://huggingface.co/Fredr0id/proquolm). This is used by default.\n\n## Installation\n\n### From PyPi\n**Note**: Both tools are part of the same PyPi package. So the following command installs both.\n\n~~~\npip install ProQuo\n~~~\n\n### From Source\nCheckout this repository and then run:\n\n~~~\npython -m pip install .\n~~~\n\n### Dependencies\nBoth installation methods install all dependencies except `tensorflow` which needs to be installed manually depending on\nthe individual needs, see [Tensorflow installation](https://www.tensorflow.org/install). The latest version that was tested is 2.14.1.\n\nFor `RelationModelLstmTrainer`, `tensorflow-text` is needed. `RelationModelLstmTrainer` should normally not be needed as\n`RelationModelBertTrainer` performs better and is the default in the pipeline.\n\n## Usage\nThe following sections describe how to use ProQuo on the command line.\n\n### Quotation detection\nTo run `ProQuoLM` with the default model, use the following command:\n\n~~~\nproquolm compare path_to_source_text path_to_target_text --text --output-type text\n~~~\n\n<details>\n<summary>All ProQuoLM command line options</summary>\n\n~~~\nusage: proquolm compare [-h] [--tokenizer TOKENIZER] [--model MODEL]\n [--lower-case | --no-lower-case]\n [--output-folder-path OUTPUT_FOLDER_PATH]\n [--create-dated-subfolder | --no-create-dated-subfolder]\n [--text | --no-text] [--output-type {json,text,csv}]\n [--csv-sep CSV_SEP] [--open-quote OPEN_QUOTE]\n [--close-quote CLOSE_QUOTE]\n [--include-long-matches-in-result]\n [--max-num-processes MAX_NUM_PROCESSES]\n source-file-path target-path\n\nProQuoLm compare allows the user to find short quotations (<= 4 words) in two\ntexts, a source text and a target text. The target text is the text quoting\nthe source text. Quotations in the target text need to be clearly marked with\nquotations marks.\n\npositional arguments:\n source-file-path Path to the source text file\n target-path Path to the target text file or folder\n\noptions:\n -h, --help show this help message and exit\n --tokenizer TOKENIZER\n Name of the tokenizer to load from Hugging Face or\n path to the tokenizer folder\n --model MODEL Name of the model to load from Hugging Face or path to\n the model folder\n --lower-case, --no-lower-case\n Run model inference on lower case text (default: True)\n --output-folder-path OUTPUT_FOLDER_PATH\n The output folder path. If this option is set the\n output will be saved to a file created in the\n specified folder\n --create-dated-subfolder, --no-create-dated-subfolder\n Create a subfolder named with the current date to\n store the results (default: False)\n --text, --no-text Include matched text in the returned data structure\n (default: True)\n --output-type {json,text,csv}\n The output type\n --csv-sep CSV_SEP output separator for csv (default: '\\t')\n --open-quote OPEN_QUOTE\n The quotation open character. If this option is not\n set, then the type of quotation marks used in a target\n text is auto automatically identified.\n --close-quote CLOSE_QUOTE\n The quotation close character. If this option is not\n set, then the type of quotation marks used in a target\n text is auto automatically identified.\n --include-long-matches-in-result\n Include matches longer than 4 words in the output\n --max-num-processes MAX_NUM_PROCESSES\n Maximum number of processes to use for parallel\n processing. This can significantly speed up the\n process.\n~~~\n\n</details>\n\nTo run `ProQuo`, use the following command:\n\n~~~\nproquo compare path_to_source_text path_to_target_text\npath_to_the_reference_vocab_file\npath_to_the_reference_model_file\npath_to_the_relation_tokenizer_folder\npath_to_the_relation_model_folder\n--text\n--output-type text\n~~~\n\n`--output-type text` prints the results to the command line. To save the results to a file, use `--output-type csv` or\n`--output-type json`. `--text` includes the quotation text in the output.\n\nThe output will look something like this:\n\n~~~\n10 15\t 500\t505\tquote\tquote\n1000\t1016\t20\t36\tsome other quote\tsome other quote\n~~~\n\nThe first two numbers are the character start and end positions in the source text and the other two numbers are the\ncharacter start and end positions in the target text.\n\n<details>\n<summary>All ProQuo command line options</summary>\n\n~~~\nusage: proquo compare [-h] [--quid-match-path QUID_MATCH_PATH]\n [--output-folder-path OUTPUT_FOLDER_PATH]\n [--create-dated-subfolder] [--no-create-dated-subfolder]\n [--parallel-print-files [PARALLEL_PRINT_FILES ...]]\n [--parallel-print-first-page PARALLEL_PRINT_FIRST_PAGE]\n [--parallel-print-last-page PARALLEL_PRINT_LAST_PAGE]\n [--text] [--no-text] [--ref] [--no-ref]\n [--output-type {json,text,csv}] [--csv-sep CSV_SEP]\n [--open-quote OPEN_QUOTE] [--close-quote CLOSE_QUOTE]\n [--include-long-matches-in-result]\n [--max-num-processes MAX_NUM_PROCESSES]\n source-file-path target-path ref-vocab-file-path\n ref-model-file-path rel-tokenizer-folder-path\n rel-model-folder-path\n\nProQuo compare allows the user to find short quotations (<= 4 words) in two\ntexts, a source text and a target text. The target text is the text quoting\nthe source text. Quotations in the target text need to be clearly marked with\nquotations marks.\n\npositional arguments:\n source-file-path Path to the source text file\n target-path Path to the target text file or folder\n ref-vocab-file-path Path to the reference vocab text file\n ref-model-file-path Path to the reference model file\n rel-tokenizer-folder-path\n Path to the relation tokenizer folder\n rel-model-folder-path\n Path to the relation model folder\n\noptions:\n -h, --help show this help message and exit\n --quid-match-path QUID_MATCH_PATH\n Path to the file or folder with quid matches. If this\n option is not set, then Quid is used to find long\n matches.\n --output-folder-path OUTPUT_FOLDER_PATH\n The output folder path. If this option is set the\n output will be saved to a file created in the\n specified folder\n --create-dated-subfolder\n Create a subfolder named with the current date to\n store the results\n --no-create-dated-subfolder\n Do not create a subfolder named with the current date\n to store the results\n --parallel-print-files [PARALLEL_PRINT_FILES ...]\n Filenames of files which quote a parallel print\n edition\n --parallel-print-first-page PARALLEL_PRINT_FIRST_PAGE\n Number of the first page with parallel print\n --parallel-print-last-page PARALLEL_PRINT_LAST_PAGE\n Number of the last page with parallel print\n --text Include matched text in the returned data structure\n --no-text Do not include matched text in the returned data\n structure\n --ref Include matched reference in the returned data\n structure\n --no-ref Do not include matched reference in the returned data\n structure\n --output-type {json,text,csv}\n The output type\n --csv-sep CSV_SEP output separator for csv (default: '\\t')\n --open-quote OPEN_QUOTE\n The quotation open character. If this option is not\n set, then the type of quotation marks used in a target\n text is auto automatically identified.\n --close-quote CLOSE_QUOTE\n The quotation close character. If this option is not\n set, then the type of quotation marks used in a target\n text is auto automatically identified.\n --include-long-matches-in-result\n Include matches longer than 4 words in the output\n --max-num-processes MAX_NUM_PROCESSES\n Maximum number of processes to use for parallel\n processing.This can significantly speed up the\n process.\n~~~\n\n</details>\n\n## Parallel processing\n`ProQuo` and `ProQuoLM` use [Quid](https://scm.cms.hu-berlin.de/schluesselstellen/quid) in the background which supports\nusing multiple processes when comparing multiple target texts with the source texts. To use Quid with multiple processes\nthe command line option `--max-num-processes` is used. The default is 1.\n\n## Training\nThe library also supports training and testing of custom models. The [Training Readme](Training-Readme.md) gives an introduction to\ntraining models.\n\n## Citation\nIf you use `ProQuo` or `ProQuoLM` or base your work on our code, please cite our paper:\n~~~\n@article{arnold2023,\n author = {Frederik Arnold, Robert J\u00e4schke},\n title = {A Novel Approach for Identification and Linking of Short Quotations in Scholarly Texts and Literary Works},\n volume = {2},\n year = {2023},\n url = {https://jcls.io/article/id/3590/},\n issue = {1},\n doi = {10.48694/jcls.3590},\n month = {1},\n publisher={Universit\u00e4ts- und Landesbibliothek Darmstadt},\n journal = {Journal of Computational Literary Studies}\n}\n~~~\n",
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