# Analiticcl
## Introduction
Analiticcl is an approximate string matching or fuzzy-matching system that can be used for spelling
correction or text normalisation (such as post-OCR correction or post-HTR correction). Texts can be checked against a
validated or corpus-derived lexicon (with or without frequency information) and spelling variants will be returned.
Please see the [main README.md](../../README.md) for a further introduction, it also links to a Python tutorial.
Analiticcl is written in Rust, this is the Python binding, allowing you to use analiticcl from Python as a module.
## Installation
### with pip
```
pip install analiticcl
```
### from source
To use this method, you need to have Rust installed and in your ``$PATH``. Install it through your package manager or through rustup:
```
curl https://sh.rustup.rs -sSf | sh -s -- -y
export PATH="$HOME/.cargo/bin:$PATH"
```
Once Rust is installed, you can compile the analiticcl binding:
```
# Create a virtual env (you can use yours as well)
python -m venv .env
source .env/bin/activate
# Install `analiticcl` in the current virtual env
pip install setuptools_rust
python setup.py install
```
## Usage
```python
from analiticcl import VariantModel, Weights, SearchParameters
import json
model = VariantModel("examples/simple.alphabet.tsv", Weights(), debug=False)
model.read_lexicon("examples/eng.aspell.lexicon")
model.build()
result = model.find_variants("udnerstand", SearchParameters(max_edit_distance=3))
print(json.dumps(result, ensure_ascii=False, indent=4))
print()
results = model.find_all_matches("I do not udnerstand the probleem", SearchParameters(max_edit_distance=3,max_ngram=1))
print(json.dumps(results, ensure_ascii=False, indent=4))
```
**Note:** all offsets reported by analiticcl are utf-8 byte-offsets, not character offsets! If you want proper unicode character
offsets, pass the keyword argument `unicodeoffset=True` to `SearchParameters`. You will want to set this if you intend to do
any kind of slicing in Python (which uses unicode points by default).
Output:
```json
[
{
"text": "understand",
"score": 0.8978494623655915,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understands",
"score": 0.6725317693059629,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understood",
"score": 0.6036866359447004,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understate",
"score": 0.5967741935483871,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
```
```json
[
{
"input": "I",
"offset": {
"begin": 0,
"end": 1
},
"variants": [
{
"text": "I",
"score": 0.8387096774193549,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "i",
"score": 0.8064516129032258,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
},
{
"input": "do",
"offset": {
"begin": 2,
"end": 4
},
"variants": [
{
"text": "do",
"score": 1.0,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "dog",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "doc",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "doz",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "dob",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "doe",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "dot",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "dos",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "ado",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "don",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "d",
"score": 0.5967741935483871,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "o",
"score": 0.5967741935483871,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "DOD",
"score": 0.5913978494623655,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
},
{
"input": "not",
"offset": {
"begin": 5,
"end": 8
},
"variants": [
{
"text": "not",
"score": 1.0,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "knot",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "note",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "snot",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "no",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "nowt",
"score": 0.5967741935483871,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "No",
"score": 0.5913978494623655,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "OT",
"score": 0.5913978494623655,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "pot",
"score": 0.5698924731182795,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
},
{
"input": "udnerstand",
"offset": {
"begin": 9,
"end": 19
},
"variants": [
{
"text": "understand",
"score": 0.8978494623655915,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understands",
"score": 0.6725317693059629,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understood",
"score": 0.6036866359447004,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understate",
"score": 0.5967741935483871,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
},
{
"input": "the",
"offset": {
"begin": 20,
"end": 23
},
"variants": [
{
"text": "the",
"score": 1.0,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "thee",
"score": 0.6908602150537635,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "thew",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "then",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "them",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "they",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "he",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "Thea",
"score": 0.6048387096774194,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "Th",
"score": 0.5913978494623655,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "He",
"score": 0.5913978494623655,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "thy",
"score": 0.5698924731182795,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "she",
"score": 0.5698924731182795,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "tho",
"score": 0.5698924731182795,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "Thu",
"score": 0.5376344086021505,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "Che",
"score": 0.5376344086021505,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "THC",
"score": 0.5376344086021505,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "tee",
"score": 0.5161290322580645,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "toe",
"score": 0.5161290322580645,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "tie",
"score": 0.5161290322580645,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "Te",
"score": 0.510752688172043,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
},
{
"input": "probleem",
"offset": {
"begin": 24,
"end": 32
},
"variants": [
{
"text": "problem",
"score": 0.9231950844854071,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "problems",
"score": 0.6908602150537635,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "probe",
"score": 0.5913978494623656,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "proclaim",
"score": 0.5766129032258065,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "probated",
"score": 0.543010752688172,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "probates",
"score": 0.543010752688172,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "prole",
"score": 0.5322580645161291,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "prowlers",
"score": 0.4959677419354839,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "parolees",
"score": 0.44220430107526887,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
}
]
```
## Documentation
The python binding exposes only a minimal interface, you can use Python's ``help()`` function to get information on the
classes provided. For more detailed information, please consult the [Analiticcl's rust API documentation](https://docs.rs/analiticcl/). The interfaces that are available in the binding are analogous to the rust versions.
Raw data
{
"_id": null,
"home_page": "https://github.com/proycon/analiticcl",
"name": "analiticcl",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "nlp, text-processing, spelling-correction, linguistics, spellcheck",
"author": "Maarten van Gompel <proycon@anaproy.nl>",
"author_email": "Maarten van Gompel <proycon@anaproy.nl>",
"download_url": "https://files.pythonhosted.org/packages/c7/cf/ed8792c678e189fbe8668dff121003b1c48a39e848d893cf09d6a9226371/analiticcl-0.4.6.tar.gz",
"platform": null,
"description": "\n# Analiticcl\n\n## Introduction\n\nAnaliticcl is an approximate string matching or fuzzy-matching system that can be used for spelling\ncorrection or text normalisation (such as post-OCR correction or post-HTR correction). Texts can be checked against a\nvalidated or corpus-derived lexicon (with or without frequency information) and spelling variants will be returned.\n\nPlease see the [main README.md](../../README.md) for a further introduction, it also links to a Python tutorial.\n\nAnaliticcl is written in Rust, this is the Python binding, allowing you to use analiticcl from Python as a module.\n\n## Installation\n\n### with pip\n\n```\npip install analiticcl\n```\n\n### from source\n\nTo use this method, you need to have Rust installed and in your ``$PATH``. Install it through your package manager or through rustup:\n\n```\ncurl https://sh.rustup.rs -sSf | sh -s -- -y\nexport PATH=\"$HOME/.cargo/bin:$PATH\"\n```\nOnce Rust is installed, you can compile the analiticcl binding:\n\n```\n# Create a virtual env (you can use yours as well)\npython -m venv .env\nsource .env/bin/activate\n\n# Install `analiticcl` in the current virtual env\npip install setuptools_rust\npython setup.py install\n```\n\n## Usage\n\n```python\nfrom analiticcl import VariantModel, Weights, SearchParameters\nimport json\n\nmodel = VariantModel(\"examples/simple.alphabet.tsv\", Weights(), debug=False)\nmodel.read_lexicon(\"examples/eng.aspell.lexicon\")\nmodel.build()\nresult = model.find_variants(\"udnerstand\", SearchParameters(max_edit_distance=3))\nprint(json.dumps(result, ensure_ascii=False, indent=4))\nprint()\nresults = model.find_all_matches(\"I do not udnerstand the probleem\", SearchParameters(max_edit_distance=3,max_ngram=1))\nprint(json.dumps(results, ensure_ascii=False, indent=4))\n```\n\n**Note:** all offsets reported by analiticcl are utf-8 byte-offsets, not character offsets! If you want proper unicode character\noffsets, pass the keyword argument `unicodeoffset=True` to `SearchParameters`. You will want to set this if you intend to do\nany kind of slicing in Python (which uses unicode points by default).\n\n\nOutput:\n\n```json\n[\n {\n \"text\": \"understand\",\n \"score\": 0.8978494623655915,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"understands\",\n \"score\": 0.6725317693059629,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"understood\",\n \"score\": 0.6036866359447004,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"understate\",\n \"score\": 0.5967741935483871,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n }\n]\n```\n\n```json\n[\n {\n \"input\": \"I\",\n \"offset\": {\n \"begin\": 0,\n \"end\": 1\n },\n \"variants\": [\n {\n \"text\": \"I\",\n \"score\": 0.8387096774193549,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"i\",\n \"score\": 0.8064516129032258,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n }\n ]\n },\n {\n \"input\": \"do\",\n \"offset\": {\n \"begin\": 2,\n \"end\": 4\n },\n \"variants\": [\n {\n \"text\": \"do\",\n \"score\": 1.0,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"dog\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"doc\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"doz\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"dob\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"doe\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"dot\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"dos\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"ado\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"don\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"d\",\n \"score\": 0.5967741935483871,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"o\",\n \"score\": 0.5967741935483871,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"DOD\",\n \"score\": 0.5913978494623655,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n }\n ]\n },\n {\n \"input\": \"not\",\n \"offset\": {\n \"begin\": 5,\n \"end\": 8\n },\n \"variants\": [\n {\n \"text\": \"not\",\n \"score\": 1.0,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"knot\",\n \"score\": 0.6370967741935484,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"note\",\n \"score\": 0.6370967741935484,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"snot\",\n \"score\": 0.6370967741935484,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"no\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"nowt\",\n \"score\": 0.5967741935483871,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"No\",\n \"score\": 0.5913978494623655,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"OT\",\n \"score\": 0.5913978494623655,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"pot\",\n \"score\": 0.5698924731182795,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n }\n ]\n },\n {\n \"input\": \"udnerstand\",\n \"offset\": {\n \"begin\": 9,\n \"end\": 19\n },\n \"variants\": [\n {\n \"text\": \"understand\",\n \"score\": 0.8978494623655915,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"understands\",\n \"score\": 0.6725317693059629,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"understood\",\n \"score\": 0.6036866359447004,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"understate\",\n \"score\": 0.5967741935483871,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n }\n ]\n },\n {\n \"input\": \"the\",\n \"offset\": {\n \"begin\": 20,\n \"end\": 23\n },\n \"variants\": [\n {\n \"text\": \"the\",\n \"score\": 1.0,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"thee\",\n \"score\": 0.6908602150537635,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"thew\",\n \"score\": 0.6370967741935484,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"then\",\n \"score\": 0.6370967741935484,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"them\",\n \"score\": 0.6370967741935484,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"they\",\n \"score\": 0.6370967741935484,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"he\",\n \"score\": 0.6236559139784946,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"Thea\",\n \"score\": 0.6048387096774194,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"Th\",\n \"score\": 0.5913978494623655,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"He\",\n \"score\": 0.5913978494623655,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"thy\",\n \"score\": 0.5698924731182795,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"she\",\n \"score\": 0.5698924731182795,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"tho\",\n \"score\": 0.5698924731182795,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"Thu\",\n \"score\": 0.5376344086021505,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"Che\",\n \"score\": 0.5376344086021505,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"THC\",\n \"score\": 0.5376344086021505,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"tee\",\n \"score\": 0.5161290322580645,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"toe\",\n \"score\": 0.5161290322580645,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"tie\",\n \"score\": 0.5161290322580645,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"Te\",\n \"score\": 0.510752688172043,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n }\n ]\n },\n {\n \"input\": \"probleem\",\n \"offset\": {\n \"begin\": 24,\n \"end\": 32\n },\n \"variants\": [\n {\n \"text\": \"problem\",\n \"score\": 0.9231950844854071,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"problems\",\n \"score\": 0.6908602150537635,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"probe\",\n \"score\": 0.5913978494623656,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"proclaim\",\n \"score\": 0.5766129032258065,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"probated\",\n \"score\": 0.543010752688172,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"probates\",\n \"score\": 0.543010752688172,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"prole\",\n \"score\": 0.5322580645161291,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"prowlers\",\n \"score\": 0.4959677419354839,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n },\n {\n \"text\": \"parolees\",\n \"score\": 0.44220430107526887,\n \"lexicon\": \"../../../examples/eng.aspell.lexicon\"\n }\n ]\n }\n]\n\n```\n\n## Documentation\n\nThe python binding exposes only a minimal interface, you can use Python's ``help()`` function to get information on the\nclasses provided. For more detailed information, please consult the [Analiticcl's rust API documentation](https://docs.rs/analiticcl/). The interfaces that are available in the binding are analogous to the rust versions.\n\n",
"bugtrack_url": null,
"license": "GPL-3.0+",
"summary": "Analiticcl is an approximate string matching or fuzzy-matching system that can be used to find variants for spelling correction or text normalisation",
"version": "0.4.6",
"project_urls": {
"Homepage": "https://github.com/proycon/analiticcl",
"Source Code": "https://github.com/proycon/analiticcl"
},
"split_keywords": [
"nlp",
" text-processing",
" spelling-correction",
" linguistics",
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