yake


Nameyake JSON
Version 0.4.8 PyPI version JSON
download
home_pagehttps://pypi.python.org/pypi/yake
SummaryKeyword extraction Python package
upload_time2021-04-26 22:52:31
maintainer
docs_urlNone
author
requires_python
licenseLGPLv3
keywords yake
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            
# Yet Another Keyword Extractor (Yake)

Unsupervised Approach for Automatic Keyword Extraction using Text Features.

YAKE! is a light-weight unsupervised automatic keyword extraction method which rests on text statistical features extracted from single documents to select the most important keywords of a text. Our system does not need to be trained on a particular set of documents, neither it depends on dictionaries, external-corpus, size of the text, language or domain. To demonstrate the merits and the significance of our proposal, we compare it against ten state-of-the-art unsupervised approaches (TF.IDF, KP-Miner, RAKE, TextRank, SingleRank, ExpandRank, TopicRank, TopicalPageRank, PositionRank and MultipartiteRank), and one supervised method (KEA). Experimental results carried out on top of twenty datasets (see Benchmark section below) show that our methods significantly outperform state-of-the-art methods under a number of collections of different sizes, languages or domains. In addition to the python package here described, we also make available a <a href="http://yake.inesctec.pt" target="_blank">demo</a>, an <a href="http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords" target="_blank">API</a> and a <a href="https://play.google.com/store/apps/details?id=com.yake.yake" target="_blank">mobile app</a>.

## Main Features

* Unsupervised approach
* Corpus-Independent
* Domain and Language Independent
* Single-Document

## Where can I find YAKE!?
YAKE! is available online [http://yake.inesctec.pt], as an open source Python package [https://github.com/LIAAD/yake] and on [Google Play](https://play.google.com/store/apps/details?id=com.yake.yake).

## References
Please cite the following works when using YAKE

<b>In-depth journal paper at Information Sciences Journal</b>

Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509, pp 257-289. [pdf](https://doi.org/10.1016/j.ins.2019.09.013)

<b>ECIR'18 Best Short Paper</b>

Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 684 - 691. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_63)

Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). YAKE! Collection-independent Automatic Keyword Extractor. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 806 - 810. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_80)

## Awards
[ECIR'18](http://ecir2018.org) Best Short Paper

Raw data

            {
    "_id": null,
    "home_page": "https://pypi.python.org/pypi/yake",
    "name": "yake",
    "maintainer": "",
    "docs_url": null,
    "requires_python": "",
    "maintainer_email": "",
    "keywords": "yake",
    "author": "",
    "author_email": "",
    "download_url": "https://files.pythonhosted.org/packages/7a/95/b4091038c7fa99408f0878070cf11f6b4d6d2675461b7e80848482608c52/yake-0.4.8.tar.gz",
    "platform": "",
    "description": "\n# Yet Another Keyword Extractor (Yake)\n\nUnsupervised Approach for Automatic Keyword Extraction using Text Features.\n\nYAKE! is a light-weight unsupervised automatic keyword extraction method which rests on text statistical features extracted from single documents to select the most important keywords of a text. Our system does not need to be trained on a particular set of documents, neither it depends on dictionaries, external-corpus, size of the text, language or domain. To demonstrate the merits and the significance of our proposal, we compare it against ten state-of-the-art unsupervised approaches (TF.IDF, KP-Miner, RAKE, TextRank, SingleRank, ExpandRank, TopicRank, TopicalPageRank, PositionRank and MultipartiteRank), and one supervised method (KEA). Experimental results carried out on top of twenty datasets (see Benchmark section below) show that our methods significantly outperform state-of-the-art methods under a number of collections of different sizes, languages or domains. In addition to the python package here described, we also make available a <a href=\"http://yake.inesctec.pt\" target=\"_blank\">demo</a>, an <a href=\"http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords\" target=\"_blank\">API</a> and a <a href=\"https://play.google.com/store/apps/details?id=com.yake.yake\" target=\"_blank\">mobile app</a>.\n\n## Main Features\n\n* Unsupervised approach\n* Corpus-Independent\n* Domain and Language Independent\n* Single-Document\n\n## Where can I find YAKE!?\nYAKE! is available online [http://yake.inesctec.pt], as an open source Python package [https://github.com/LIAAD/yake] and on [Google Play](https://play.google.com/store/apps/details?id=com.yake.yake).\n\n## References\nPlease cite the following works when using YAKE\n\n<b>In-depth journal paper at Information Sciences Journal</b>\n\nCampos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509, pp 257-289. [pdf](https://doi.org/10.1016/j.ins.2019.09.013)\n\n<b>ECIR'18 Best Short Paper</b>\n\nCampos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 \u2013 29). Lecture Notes in Computer Science, vol 10772, pp. 684 - 691. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_63)\n\nCampos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). YAKE! Collection-independent Automatic Keyword Extractor. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 \u2013 29). Lecture Notes in Computer Science, vol 10772, pp. 806 - 810. [pdf](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_80)\n\n## Awards\n[ECIR'18](http://ecir2018.org) Best Short Paper\n\n\n",
    "bugtrack_url": null,
    "license": "LGPLv3",
    "summary": "Keyword extraction Python package",
    "version": "0.4.8",
    "split_keywords": [
        "yake"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "ff7fc4de4fb40639ec674f944d82e5b0be5a5a9162fc8e83e379ab10b83ee1f9",
                "md5": "59c173e7ad2e5c13dbab86b6ad5e841e",
                "sha256": "d46793266826468b4aecb668c51e677b7bc304f1bd3a15e100e324852ec5a0c3"
            },
            "downloads": -1,
            "filename": "yake-0.4.8-py2.py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "59c173e7ad2e5c13dbab86b6ad5e841e",
            "packagetype": "bdist_wheel",
            "python_version": "py2.py3",
            "requires_python": null,
            "size": 60162,
            "upload_time": "2021-04-26T22:52:20",
            "upload_time_iso_8601": "2021-04-26T22:52:20.704373Z",
            "url": "https://files.pythonhosted.org/packages/ff/7f/c4de4fb40639ec674f944d82e5b0be5a5a9162fc8e83e379ab10b83ee1f9/yake-0.4.8-py2.py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "7a95b4091038c7fa99408f0878070cf11f6b4d6d2675461b7e80848482608c52",
                "md5": "02eaafd91a226f18398b53b11a8fb120",
                "sha256": "859f379ac49ca204a0bc1527217f937321e87b68287f81db9700fc1039fd529a"
            },
            "downloads": -1,
            "filename": "yake-0.4.8.tar.gz",
            "has_sig": false,
            "md5_digest": "02eaafd91a226f18398b53b11a8fb120",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 404284,
            "upload_time": "2021-04-26T22:52:31",
            "upload_time_iso_8601": "2021-04-26T22:52:31.732885Z",
            "url": "https://files.pythonhosted.org/packages/7a/95/b4091038c7fa99408f0878070cf11f6b4d6d2675461b7e80848482608c52/yake-0.4.8.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2021-04-26 22:52:31",
    "github": false,
    "gitlab": false,
    "bitbucket": false,
    "lcname": "yake"
}
Elapsed time: 0.05949s