## 2 Materials and Methods
### Method and System  
The input reports are written in different languages (Italian, English) and sometimes, from year to year, the structure of their content changes remarkably. This inherent property of the input reports represented a challenging but also interesting feature to test the proposed NPL system on, and to assess how it performs in situations when the input documents change as a reflection of a dynamic volcanological observatory monitoring activity through time. Nevertheless, items to be tagged had approximately the same structure. In fact, the corpus was composed by similar documents, where documents contained free-text sequences but in a preformatted way. Moreover, most of the parameters useful to monitor a volcano are numeric data, and the extraction of numeric data from text is not complex enough to require building a proper NER algorithm. This encourages the use of _'regular expressions'_ (regex). A regex is basically a special character sequence that helps to match or find other strings or sets of strings using that sequence as a search pattern. Indeed, once identified, patterns may be used in a predictable manner. Regex is one of the rule-based pattern search methods in text mining, which is based on the assumption that key information to find occurs in a recurrent form [5,7]. Indeed, this method is especially suitable for contents with significant syntactic properties (such as number, date, acronym) and for information extraction tasks where contexts around the underlying fine information are not important in locating target information. We are interested in entity recognition on two levels. The first is for recognizing mentions of parameters within text. Once the list is built, the exact matches may be sufficient, but in other cases, stemming is necessary to match the word's base form (i.e., lemmatization). A second level of entity recognition regards numeric values jointly to measurement units. Further step is to correctly associate the occurrence of a parameter with its value and, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Daily** & **Weekly** \\ \hline
2015 & 0 & 24 \\
2016 & 0 & 25 \\
2017 & 0 & 24 \\
2018 & 0 & 25 \\
2019 & 69 & 39 \\
2020 & 0 & 52 \\
2021 & 0 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of bulletins in the INGV dataset. finally, to associate the temporal dimension. The IE task we defined is expressed through the following formal relation:  
_Measures(parameter, value, date)_  
On the basis of previous assumptions and motivations, we designed and developed a NLP system implementing the following steps:  
* Corpus download and processing;
* Manual consultation and tagging;
* Automatic tagging and rule base refinement;
* Parameter validation;
* Visual exploration of time series. The system was developed under PyCharm 2020.3 environment on Python 3.8 interpreter (up to 3.7.2 version). The system was implemented by fully exploiting Python library capabilities. A general architecture of the system is reported in Figure 2, while Figure 3 shows the workflow implemented for the text processing phase. After the download phase, expert-users performed some sessions of manual consultation of text in order to: (1) define the set of categories of relevant parameters (i.e., tags); (2) gain knowledge about recurring patterns of interest and keywords identifying parameters; and (3) catch language and content structure peculiarities to incrementally draft regular expressions useful in extracting values for parameters. This was performed by using the embedded graphical user interface (GUI) developed in our system on the PyQt5 library. After the selection of the institute issuing the bulletin and the date of interest, the user may read a report content, apply regexes and refine these when the match fails.