Cso-classifier

Version alignement with Pypi. Similar to version 2.3.1.

Bug Fix. Added some exception handles

This new release, contains a bug fix and the latest version of the CSO ontology.

Bug Fix: When running in batch mode, the classifier was treating the keyword field as an array instead of string. In this way, instead of processing keywords (separated by comma), it was processing each single letters, hence inferring wrong topics. This now has been fixed. In addition, if the keyword field is actually an array, the classifier will first 'stringify' it and then process it.

We also downloaded and packed the latest version of the CSO ontology.

In this version (release v2.2), we (i) updated the requirements needed to run the classifier, (ii) removed all unnecessary warnings, and (iii) enabled multiprocessing. In particular, we removed all useless requirements that were installed in development mode, by cleaning the _requirements.txt_ file.

When computing certain research papers, the classifier display warnings raised by the [kneed library](https://pypi.org/project/kneed/). Since the classifier can automatically adapt to such warnings, we decided to hide them and prevent users from being concerned about such outcome.

This version of the classifier provides improved **scalablibility** through multiprocessing. Once the number of workers is set (i.e. num_workers >= 1), each worker will be given a copy of the CSO Classifier with a chunk of the corpus to process. Then, the results will be aggregated once all processes are completed. Please be aware that this function is only available in batch mode.

The CSO Classifier is an application that takes as input the text from abstract, title, and keywords of a research paper and outputs a list of relevant concepts from CSO. This new release (version v2.1) aims at improving its scalability.
Compared to its previous version (v2.0), the classifier relies on a cached word2vec model which connects the words within the model vocabulary directly with the CSO topics. Thanks to this cache, the classifier is able to quickly retrieve all CSO topics that could be inferred by given tokens, speeding up the processing time. In addition, this cache is lighter (~64M) compared to the actual word2vec model (~366MB), which allows to save additional time at loading time.
Thanks to this improvement the CSO Classifier is around 24x faster and can be easily run on large corpus of scholarly data.

Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this repository, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of research areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.