A newly proposed knowledge-based WSD approach, KEF (#430)

Co-authored-by: Sebastian Ruder <ruder.sebastian@gmail.com>

english/word_sense_disambiguation.md

@@ -47,10 +47,11 @@ The main evaluation measure is F1-score.
 | Model           | All | Senseval 2 |Senseval 3 |SemEval 2007 |SemEval 2013 |SemEval 2015 |  Paper / Source |
 | ------------- | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | --- |
 |WN 1st sense baseline | 65.2 | 66.8 | 66.2 | 55.2 | 63.0 | 67.8 | [[1]](http://aclweb.org/anthology/E/E17/E17-1010.pdf) |
-|Babelfy | 65.5 | 67.0 | 63.5 | 51.6 | 66.4 | **70.3** | [[8]](http://aclweb.org/anthology/Q14-1019) |
+|Babelfy | 65.5 | 67.0 | 63.5 | 51.6 | 66.4 | 70.3 | [[8]](http://aclweb.org/anthology/Q14-1019) |
 |UKB<sub>ppr_w2w-nf</sub> | 57.5 | 64.2 | 54.8 | 40.0 | 64.5 | 64.5 | [[9]](https://www.mitpressjournals.org/doi/full/10.1162/COLI_a_00164) [[12]](http://aclweb.org/anthology/W18-2505) |
-|UKB<sub>ppr_w2w</sub> | **67.3** | 68.8 | 66.1 | 53.0 | **68.8** | **70.3** | [[9]](https://www.mitpressjournals.org/doi/full/10.1162/COLI_a_00164) [[12]](http://aclweb.org/anthology/W18-2505) |
-|WSD-TM | 66.9 | **69.0** | **66.9** | **55.6** | 65.3 | 69.6 | [[10]](https://arxiv.org/pdf/1801.01900.pdf) |
+|UKB<sub>ppr_w2w</sub> | 67.3 | 68.8 | 66.1 | 53.0 | **68.8** | 70.3 | [[9]](https://www.mitpressjournals.org/doi/full/10.1162/COLI_a_00164) [[12]](http://aclweb.org/anthology/W18-2505) |
+|WSD-TM | 66.9 | 69.0 | **66.9** | 55.6 | 65.3 | 69.6 | [[10]](https://arxiv.org/pdf/1801.01900.pdf) |
+|KEF | **68.0** | **69.6** | 66.1 | **56.9** | 68.4 | **72.3** | [[16]](https://doi.org/10.1016/j.knosys.2019.105030) [[code]](https://github.com/lwmlyy/Knowledge-based-WSD)|
 
 Note: 'All' is the concatenation of all datasets, as described in [10] and [12]. The scores of [6,7] and [9] are not taken from the original papers but from the results of the implementations of [11] and [12], respectively.
 
@@ -84,13 +85,15 @@ Note: 'All' is the concatenation of all datasets, as described in [10] and [12].
 
 [15] [Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation](https://arxiv.org/abs/1905.05677)
 
+[16] [Word Sense Disambiguation: A Comprehensive Knowledge Exploitation Framework](https://doi.org/10.1016/j.knosys.2019.105030)
+
+
 ## WSD Lexical Sample task:
 
 Above task is called All-words WSD because the systems attempt to disambiguate all of the words in a document, while there is another task which is called 
 Lexical Sample task. In this task a number of words are selected and the system should only disambiguate the occurrences of these words in a test set. 
 Iaccobacci et, al. (2016) provide the state-of-the-art results until 2016 [1]. Main tasks include Senseval 2, Senseval 3  and SemEval 2007. Evaluation metrics are as same as All words task. 
 
-
 ### Lexical Sample results:
 
 | Model           | Senseval 2  |Senseval 3  |SemEval 2007 | Paper / Source |