Name
Abstract | ||
|---|---|---|
Contracts are the main medium through which parties formalize their trade relations, be they the exchange of goods or the specification of mutual obligations. While electronic contracts allow automated processes to verify their correctness, most agreements in the real world are still written in natural language, which need substantial human revision effort to eliminate possible conflicting statements in long and complex contracts. In this paper, we formalize a typology of conflict types between clauses suitable for machine learning and develop techniques to review contracts by learning to identify and classify such conflicts, facilitating the task of contract revision. We evaluate the effectiveness of our techniques using a manually annotated contract conflict corpus with results close to the current state-of-the-art for conflict identification, while introducing a more complex classification task of such conflicts for which our method surpasses the state-of-the art method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5555/3306127.3331911 | adaptive agents and multi-agents systems |
Keywords | Field | DocType |
Natural Language Processing,Norms,Norm Conflicts,Semantic Representation | Computer science,Correctness,Typology,Electronic contracts,Natural language,Natural language processing,Artificial intelligence,Semantic representation,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| João Paulo Aires | 1 | 1 | 1.39 |
| Roger L. Granada | 2 | 20 | 7.33 |
| Juarez Monteiro | 3 | 0 | 0.34 |
| Rodrigo C. Barros | 4 | 448 | 32.54 |
| Felipe Meneguzzi | 5 | 386 | 46.80 |