## 2 Materials and Methods
### Method and System
The GUI that allows the user to easily read and test the tagging during this manual phase is reported in Figure 4.  
Given the specific focus of our experiment, the set of relevant tags defined during the manual consultation of the dataset encompassed: SO\({}_{2}\) (value number and text about the  
Figure 4: Tagging: orange highlight is used for parameters and green for values/scores, when matched. The original text of the input volcanological bulletin is in Italian language. Figure 3: System workflow for text processing steps (in red). measurement unit), CO\({}_{2}\) (value number and text about the measurement unit), C/S (mixed text and number string), VLP events of the seismic signals (mixed text and number string) and satellite thermal anomalies (value number and text about the measurement unit). Occurrences of a given parameter name are identified by exploiting regex skill to formalize the word's base form and catch the word's variations on the base form (lemmatization). Once the set of rules to tag named entities is defined, rules for tagging values should be applied in co-occurrence. In this work, regular expressions were manually drafted. In particular, a subset of reports is used for manual consultation and rule base typing. For each issuing institute (INGV and LGS), this subset comprises three months per year (25% of the total dataset). The manual process of rule composition and refinement proceeds until all of the tagging cases of this subset are covered. Further support for rule accuracy is the definition of gazetteers, such as the set of measurement units. Once the set of rules is obtained, automatic extraction can be performed on the remaining dataset through a batch procedure. The implementation exploits Python built-in regex library. The regex algorithm tracks only one transition at one step, which means that the engine checks one character at a time. It supports backtracking, that is the ability to remember the last successful position, so that it can go back and retry if needed. In this way, the regex engine does not have to go back up to the start of the string in order to retry a second alternative. This optimizes the regex match. For this reason, in order to cover language and content structure diversity, more than one pattern is defined for the same tag. In total, in this work, 24 rules were exploited to extract relevant data. Preliminary text processing phase consists of converting reports (PDF, MS Word, HTML) to plain text and discarding figures and tables. Text is then split into sentences and sentences into tokens. In particular, the algorithm performs a line by line scanning of the text. Each line corresponds to a single sentence in order to perform the tagging task in the context of an auto-consistent sentence. The first match per parameter is retrieved and stored as the resulting value in the database, along with temporal information and the issuing institute. Rules are ordered on the basis of a coverage accuracy criteria computed as the number of instances for which the rule predicts correctly (i.e., support of the rule). Some simple examples of rules are:  
SO\({}_{2}\)+([0-9]+)[\(\backslash\)\(\backslash\)se]*[0-9]*\(\backslash\)st/d, which states that, when an integer number or a range of numbers is followed by a space and the string \"t/d\" (unit of measurement), then it should be marked as a SO\({}_{2}\) value;  
CO\({}_{2}\)+([0-9]+)\(\backslash\)sg\(\backslash\)sm-2 \(\backslash\)sd-1, which states that, when an integer number is followed by a space and the string \"g m-2 d-1\" (unit of measurement), then it should be marked as a CO\({}_{2}\) value;  
VLP+([0-9]+)\(\backslash\). *[0-9]*\(\backslash\)events/hour, which states that, when a decimal number is followed by a space and the string \"events/hour\" (unit of measurement), then it should be marked as a VLP value. The system provides an export functionality of data from the database, which allows the operator to produce time series to visually explore at different spatial and temporal granularity.