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Add YAKEvsBase image

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Vitor Mangaravite
Vitor Mangaravite committed Jan 10, 2019
1 parent efbdb9b commit 9bf7c0577e3589c88c57f6b9e351f763f8eb57e4
Showing with 2 additions and 2 deletions.
  1. +2 −2 README.md
  2. BIN docs/YAKEvsBaselines.jpg
@@ -3,7 +3,7 @@ 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 Comparison with state-of-the-art) 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 [__demo__](http://yake.inesctec.pt) and an [__API__](http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords).
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 [__demo__](http://yake.inesctec.pt) and an [__API__](http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords).


Main Features
@@ -17,8 +17,8 @@ Main Features
Benchmark
-------------

YAKE!, generically outperforms, statistical methods (tf.idf, kp-miner and rake), state-of-the-art graph-based methods (TextRank, SingleRank, TopicRank, TopicalPageRank, PositionRank, MultipartiteRank and ExpandRank) and supervised learning methods (KEA) across different datasets, languages and domains. The results listed in the table refer to F1 at 10 scores. Bold face marks the current best results for that specific dataset.

YAKE!, generically outperforms, statistical methods [tf.idf (in 100% of the datasets), kp-miner (in 55%) and rake (in 100%)], state-of-the-art graph-based methods [TextRank (in 100% of the datasets), SingleRank (in 90%), TopicRank (in 70%), TopicalPageRank (in 90%), PositionRank (in 90%), MultipartiteRank (in 75%) and ExpandRank (in 100%)] and supervised learning methods [KEA (in 70% of the datasets)] across different datasets, languages and domains. The results listed in the table refer to F1 at 10 scores. Bold face marks the current best results for that specific dataset. The column "Method" cites the work of the previous (or current) best method (depending where the bold face is found). The interested reader should refer to this table in order to see a detailed comparison between YAKE and all the state-of-the-art methods.

| Dataset | Language | #Docs | YAKE | Previous best | Method |
| --------------------------------------------------------------------------------- | -------- | ----- | --------- | ------------- | ---------------------------------------------------------------------------------------------------------------- |
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