## 5 Conclusions  
In this work, SPUCL was presented, a semi-supervised document classification system capable of automatically assigning a human-readable label to the documents analysed.  
The SPUCL system was tested through the analysis of a set of papers related to the state of the art of DNA methods in food inspection and regulation selected by domain experts.  
The exploitation of SPUCL to analyse the state of the art of a given scientific topic has shown the possibility to perform a semi-supervised classification of documents and an unsupervised labelling task able to provide as output a label that is semantically relevant and understandable for the humans.  
This approach can easily be applied to different research areas to facilitate the analysis of the state of the art.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{4}{c}{**Approaches**} \\ \hline
**Metics** & **[PERSON] [19]** & **[PERSON] [20]** & **[PERSON] [21]** & **[PERSON] [22]** & **SPUCL** \\ \hline Precision, Recall, F-Measure & & x & x & x \\ \hline PathRatio, Attempts, Jumps & x & & & \\ \hline Human evaluation & & x & & \\ \hline AUC & & x & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between SPUCL and the other approaches’ evaluation methods.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Label** & **Precision** & **Recall** & **F-Measure** \\ \hline “PCR methods applied to meat” & 0.7 & 0.8 & 0.75 \\ “PCR methods applied to vegetables” & 0.62 & 0.83 & 0.71 \\ “PCR methods applied to sea food” & 0.67 & 0.82 & 0.74 \\ “DNA barcode applied to meat” & 0.57 & 0.8 & 0.7 \\ “DNA barcode applied to vegetables” & 0.58 & 0.78 & 0.67 \\ “DNA barcode applied to the sea food” & 0.2 & 0.67 & 0.31 \\ “Next Generation Sequencing applied to meat” & 0.5 & 0.88 & 0.64 \\ “Next Generation Sequencing applied to vegetables” & 0.1 & 1 & 0.18 \\ “Next Generation Sequencing applied to sea food” & 0.5 & 1 & 0.67 \\ “Microarray applied to meat” & 0.5 & 1 & 0.67 \\ “Microarray applied to vegetables” & 0 & 0 & 0 \\ “Microarray applied to sea food” & 0.17 & 1 & 0.29 \\ \hline Average & 0.43 & 0.8 & 0.53 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of SPUCL.  
The SPUCL system currently requires: a set of articles related to a research field and a set of human readable labels.  
Although it is not necessary to read all the articles, it is required to recognise them as relating to the selected research field.  
In Section 2.4, papers were selected through a bibliographic search. In addition, we asked the domain experts to define the human-readable labels and to assign to each paper one of these labels.  
This can be seen as a limitation of the SPUCL system, and to overcome this limitation we intend to provide incremental training of the SPUCL system.  
The incremental training is an advantage of using neural networks.  
Some of the concepts of a given field of research learned earlier can be reused in other fields where the number of relevant papers is smaller.  
When the system is properly trained, it will be possible to ask the experts only for the set of papers to be examined and the human-readable labels.  
Conceptualisation, N.S.; Methodology, N.S. and A.P.; Validation, M.M.; Investigation, N.S.; Resources, N.S.; Data curation, N.S. and M.M.; Writing--Original draft, N.S. and M.M.; Writing--Review and editing, N.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was partially supported by POR Campania FESR 2014-2020 RAREPLAT.NET (CUP B63D18000380007) Grant from the European Social Fund. The funding sources had no involvement in the study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.  
The authors declare no conflict of interest.