Adding Lexical Sample task to English WSD (sebastianruder#425)

* Adding new task to WSD

The task of Lexical Sample was added to English WSD

* Redesigned table

The table was malformed.

* Updating Lexical Smaple

Applying @bitspilanicode comments.

english/word_sense_disambiguation.md

@@ -84,6 +84,23 @@ 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)
 
+## 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 |
+| ------------- | :-----: | :-----: | :-----: | --- |
+|IMSE + heuristics | 71.4 | 76.2  | - | [[Preprint]](http://cv.znu.ac.ir/afsharchim/pub/JofIFS2019-2.pdf) [[2]](https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs182868) |
+|IMS + Word2vec | 69.9 | 75.2  | 89.4 | [[1]](http://www.aclweb.org/anthology/P16-1085) |
+|AutoExtend | 66.5 | 73.6 | − | [[3]](https://arxiv.org/abs/1507.01127) [[4]](https://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00294)|
+|Taghipour and Ng | 66.2 | 73.4 | − | [[4]](https://www.aclweb.org/anthology/N15-1035.pdf) |
+|IMS | 65.3 | 72.9 | 87.9 | [[6]](https://www.aclweb.org/anthology/P10-4014.pdf) |
+
 ## Word Sense Induction
 
 Word sense induction (WSI) is widely known as the "unsupervised version" of WSD. The problem states as: Given a target word (e.g., "cold") and a collection of sentences (e.g., "I caught a cold", "The weather is cold") that use the word, cluster the sentences according to their different senses/meanings. We do not need to know the sense/meaning of each cluster, but sentences inside a cluster should have used the target words with the same sense.