Revert "updated the readme"

This reverts commit f673925.

README.md

@@ -82,9 +82,7 @@ In this section, we describe how we generated the word2vec model used within the
 ### Word Embedding generation
 
 We applied the word2vec approach [[2,3]](#references) to a collection of text from the Microsoft Academic Graph (MAG)  for generating word embeddings. MAG is a scientific knowledge base and a heterogeneous graph containing scientific publication records, citation relationships, authors, institutions, journals, conferences, and fields of study. It is the largest dataset of scholarly data publicly available, and, as of December 2018, it contains more than 210 million publications.
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 We first downloaded titles, and abstracts of 4,654,062 English papers in the field of Computer Science. Then we pre-processed the data by replacing spaces with underscores in all n-grams matching the CSO topic labels (e.g., “digital libraries” became “digital_libraries”) and for frequent bigrams and trigrams (e.g., “highest_accuracies”, “highly_cited_journals”). These frequent n-grams were identified by analysing combinations of words that co-occur together, as suggested in [[2]](#references) and using the parameters showed in Table 1. Indeed, while it is possible to obtain the vector of a n-gram by averaging the embedding vectors of all it words, the resulting representation usually is not as good as the one obtained by considering the n-gram as a single word during the training phase.
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 Finally, we trained the word2vec model using the parameters provided in Table 2. The parameters were set to these values after testing several combinations.
 
 | min-count  |  threshold |
@@ -102,9 +100,7 @@ Finally, we trained the word2vec model using the parameters provided in Table 2.
 
 
 After training the model, we obtained a **gensim.models.keyedvectors.Word2VecKeyedVectors** object weighing **366MB**. You can download the model [from here](https://cso.kmi.open.ac.uk/download/model.p).
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 The size of the model hindered the performance of the classifier in two ways. Firstly, it required several seconds to be loaded into memory. This was partially fixed by serialising the model file (using python pickle, see version v2.0 of CSO Classifier, ~4.5 times faster). Secondly, while processing a document, the classifier needs to retrieve the top 10 similar words for all tokens, and compare them with CSO topics. In performing such operation, the model would recquire several seconds, becoming a bottleneck for the classification process.
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 To this end, we decided to create a cached model (**token-to-cso-combined.json**) which is a dictionary that directly connects all token available in the vocabulary of the model with the CSO topics. This strategy allows to quickly retrieve all CSO topics that can be inferred by a particular token.
 
 ### token-to-cso-combined file
@@ -285,9 +281,7 @@ To observe the available settings please refer to the [Parameters](#parameters)
 
 ### Parameters
 Beside the paper(s), the function running the CSO Classifier accepts two additional parameters: (i) **modules** and (ii) **enhancement**. Both parameters are strings that define a particular behaviour for the classifier.
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 In particular, the parameter *modules* can be either "syntactic", "semantic", or "both". Using the value "syntactic", the classifier will run only the syntactic module. Using the "semantic" value, instead, the classifier will use only the semantic module. Finally, using "both", the classifier will run both syntactic and semantic modules and combine their results. The default value for *modules* is *both*.
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 The parameter *enhancement* can be either "first", "all", or "no". This parameters controls whether the classifier will try to infer, given a topic (e.g., Linked Data), only the direct super-topics (e.g., Semantic Web) or all its super-topics (e.g., Semantic Web, WWW, Computer Science). Using "first" as value, it will infer only the direct super topics. Instead, if using "all", the classifier will infer all its super-topics. Using "no" the classifier will not perform any enhancement. The default value for *enhancement* is *first*.