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Similis — The Italian Senate's AI Tool for Clustering Near-Duplicate Legislative Amendments

Italy · Rome · See the Italy profile

The Italian Senate uses Similis, an NLP tool built with CNR, to automatically cluster near-identical amendments filed en masse during filibusters. Presented in a peer-reviewed 2022 paper and released as open source, it keeps final voting-order authority with the Speaker.

Similis — The Italian Senate's AI Tool for Clustering Near-Duplicate Legislative Amendments

Details

Maturity
Established
Promoter
Senato della Repubblica (Italian Senate), Information Systems Development Office, with CNR-IGSG
Period
Developed from 2019, published 2022, operational through 2025
Keywords
legislative drafting support, NLP text-similarity clustering, parliamentary procedure automation, open-source govtech

Context

Similis is an AI/NLP text-clustering tool used by the Italian Senate (Senato della Repubblica) to detect groups of near-identical or heavily overlapping amendments submitted to bills, addressing 'filibustering' tactics where opposition parties file thousands of amendments differing by only a few words to delay a vote. It was built by the Senate's Information Systems Development Office with CNR-IGSG and is embedded in the Senate's GEM (Gestore Emendamenti) amendment-management suite.

Activities

The tool and its methodology were presented in a peer-reviewed paper at the ParlaCLARIN III workshop (LREC 2022), and its code is published as open source on GitHub (SenatoDellaRepubblica/Similis), offering a REST/Flask API and a command-line interface. Similis is positioned as assistive rather than autonomous: clustering is described as 'almost instant', but a human reviews, approves, modifies and integrates the results, with the Speaker retaining full authority over the final voting order.

Results

The Senate's own dossier states the tool 'significantly reduced processing times' for what was previously a heavily manual triage task, and the Inter-Parliamentary Union published an independent case study on how it addresses mass near-duplicate amendment filing.

Conclusions

Precise quantitative figures on hours saved, amendment volumes processed, or clustering precision/recall were not independently verifiable with hard numbers, so time-savings claims should be read as Senate self-reported; no adversarial audit or public criticism of the tool's accuracy was found.

Implementation

Implementation detail (cost, timeline, staffing, conditions for success) is not yet available for this practice.

Data sources

Where this practice's information was retrieved from, and when.

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