Merge pull request #183 from boudinfl/v2.0

V2.0

.gitignore

@@ -5,5 +5,4 @@ docs/build/html/_sources/*
 dev/
 .pytest_cache/
 /venv/
-/venv2/
 \.idea/

.travis.yml

@@ -5,8 +5,6 @@ python:
 # command to install dependencies
 install:
  - pip install -r requirements.txt
- - python -m nltk.downloader stopwords
- - python -m nltk.downloader universal_tagset
  - python -m spacy download en_core_web_sm
  - python -m pip install cython
  - python -m pip install git+https://github.com/epfml/sent2vec

README.md

@@ -25,16 +25,6 @@ To pip install `pke` from github:
 pip install git+https://github.com/boudinfl/pke.git
 ```
 
-`pke` also requires external resources that can be obtained using:
-
-```bash
-python -m nltk.downloader stopwords
-python -m nltk.downloader universal_tagset
-python -m spacy download en_core_web_sm # download the english model
-```
-
-As of April 2019, `pke` only supports Python 3.6+.
-
 ## Minimal example
 
 `pke` provides a standardized API for extracting keyphrases from a document.
@@ -47,9 +37,9 @@ import pke
 # initialize keyphrase extraction model, here TopicRank
 extractor = pke.unsupervised.TopicRank()
 
-# load the content of the document, here document is expected to be in raw
-# format (i.e. a simple text file) and preprocessing is carried out using spacy
-extractor.load_document(input='/path/to/input.txt', language='en')
+# load the content of the document, here document is expected to be a simple 
+# test string and preprocessing is carried out using spacy
+extractor.load_document(input='text', language='en')
 
 # keyphrase candidate selection, in the case of TopicRank: sequences of nouns
 # and adjectives (i.e. `(Noun|Adj)*`)
@@ -67,29 +57,33 @@ A detailed example is provided in the [`examples/`](examples/) directory.
 
 ## Getting started
 
-Tutorials and code documentation are available at
-[https://boudinfl.github.io/pke/](https://boudinfl.github.io/pke/).
+To get your hands dirty with `pke`, we invite you to try our tutorials out.
+
+| Name | Link |
+| ---------------------------------------------- | ---------- |
+| Getting started with `pke` and keyphrase extraction | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-1-graph-based-keyphrase-extraction.ipynb) |
+| Model parameterization | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-2-parameterization.ipynb) |
+| Benchmarking models | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-3-benchmarking-models.ipynb) |
 
 ## Implemented models
 
 `pke` currently implements the following keyphrase extraction models:
 
 * Unsupervised models
  * Statistical models
- * TfIdf [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#tfidf)]
- * KPMiner [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#kpminer), [article by (El-Beltagy and Rafea, 2010)](http://www.aclweb.org/anthology/S10-1041.pdf)]
- * YAKE [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#yake), [article by (Campos et al., 2020)](https://doi.org/10.1016/j.ins.2019.09.013)]
+ * FirstPhrases
+ * TfIdf
+ * YAKE [(Campos et al., 2020)](https://doi.org/10.1016/j.ins.2019.09.013)
  * Graph-based models
- * TextRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#textrank), [article by (Mihalcea and Tarau, 2004)](http://www.aclweb.org/anthology/W04-3252.pdf)]
- * SingleRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#singlerank), [article by (Wan and Xiao, 2008)](http://www.aclweb.org/anthology/C08-1122.pdf)]
- * TopicRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#topicrank), [article by (Bougouin et al., 2013)](http://aclweb.org/anthology/I13-1062.pdf)]
- * TopicalPageRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#topicalpagerank), [article by (Sterckx et al., 2015)](http://users.intec.ugent.be/cdvelder/papers/2015/sterckx2015wwwb.pdf)]
- * PositionRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#positionrank), [article by (Florescu and Caragea, 2017)](http://www.aclweb.org/anthology/P17-1102.pdf)]
- * MultipartiteRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#multipartiterank), [article by (Boudin, 2018)](https://arxiv.org/abs/1803.08721)]
+ * TextRank [(Mihalcea and Tarau, 2004)](http://www.aclweb.org/anthology/W04-3252.pdf)
+ * SingleRank [(Wan and Xiao, 2008)](http://www.aclweb.org/anthology/C08-1122.pdf)
+ * TopicRank [(Bougouin et al., 2013)](http://aclweb.org/anthology/I13-1062.pdf)
+ * TopicalPageRank [(Sterckx et al., 2015)](http://users.intec.ugent.be/cdvelder/papers/2015/sterckx2015wwwb.pdf)
+ * PositionRank [(Florescu and Caragea, 2017)](http://www.aclweb.org/anthology/P17-1102.pdf)
+ * MultipartiteRank [(Boudin, 2018)](https://arxiv.org/abs/1803.08721)
 * Supervised models
  * Feature-based models
- * Kea [[documentation](https://boudinfl.github.io/pke/build/html/supervised.html#kea), [article by (Witten et al., 2005)](https://www.cs.waikato.ac.nz/ml/publications/2005/chap_Witten-et-al_Windows.pdf)]
- * WINGNUS [[documentation](https://boudinfl.github.io/pke/build/html/supervised.html#wingnus), [article by (Nguyen and Luong, 2010)](http://www.aclweb.org/anthology/S10-1035.pdf)]
+ * Kea [(Witten et al., 2005)](https://www.cs.waikato.ac.nz/ml/publications/2005/chap_Witten-et-al_Windows.pdf)
 
 ## Citing pke
 

examples/2.txt

@@ -0,0 +1,7 @@
+Waiting for the wave to crest [wavelength services]
+Wavelength services have been hyped ad nauseam for years. But despite their
+ quick turn-up time and impressive margins, such services have yet to
+ live up to the industry's expectations. The reasons for this lukewarm
+ reception are many, not the least of which is the confusion that still
+ surrounds the technology, but most industry observers are still
+ convinced that wavelength services with ultimately flourish