Contract clause recognition is the process of discriminating legal clauses from ordinary sentences in contracts, and categorizing them accordingly. It is one of the most crucial tasks performed by human experts, typically lawyers from legal offices, when analyzing contracts, often for negotiation purposes. When manually executed, contract clause recognition is often time-consuming and error-prone. Traditional solutions based on machine learning leverage two-stepped approaches relying on anomalous contract clause detection and classification. Unfortunately, these approaches fail to deliver accurate results. In this paper, we propose a holistic approach, based on openset recognition algorithms, to the problem of contract clause recognition. Experimental results on benchmark data prove that the proposed solution is effective.

Large and medium-sized manufacturing companies are concerned with maintaining their supplier registries with well-reputed suppliers sourced over time. Every supplier periodically undergoes rigorous qualification processes where procurement officers assess, among other factors, the supplier document compliance status. To this end, procurement officers periodically ask suppliers, via digital e-procurement platforms, for qualification documents of different categories. Conversely, suppliers promptly answer by handing out such documents that can, maliciously or inadvertently, be wrong. When wrong qualification documents remain undetected, and the associated suppliers are qualified, a threat to the overall business arises: procurement officers may entrust purchase orders to not compliant suppliers with unpredictable performances. Our claim is that equipping e-procurement platforms with document recognition based on supervised open-set recognition (OSR) could mitigate the problem. In particular, we deem OSR solutions suitable for supplier qualification document recognition due to their simultaneous abilities of i) recognizing documents belonging to relevant categories and ii) rejecting those belonging to unknown categories. Quantitative and qualitative results from a real-world case study in partnership with an Italian manufacturing company show that the proposed solution is viable.

We present a method for boosting relational classifiers of individual resources in the context of the Web of Data . We show how weak classifiers induced by simple concept learners can be enhanced producing strong classification models from training datasets. Even more so the comprehensibility of the model is to some extent preserved as it can be regarded as a sort of concept in disjunctive form. We demonstrate the application of this approach to a weak learner that is easily derived from learners that search a space of hypotheses, requiring an adaptation of the underlying heuristics to take into account weighted training examples. An experimental evaluation on a variety of artificial learning problems and datasets shows that the proposed approach enhances the performance of the basic learners and is competitive, outperforming current concept learning systems.