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Figure from:Semantic Annotation of Legal Contracts with ContrattoA

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Figure 1. Contract ontology adopted from Symboleo [9] using standard UML notation for class diagrams. An ontology consists of a collection of concepts and relationships for conceptualizing a domain, in our case contracts. The ontology we adopt has been proposed in [9] for the Symboleo contract specification language and is shown in Figure 1. The ontology is based on the Core Legal Ontology UFO-L [10] and was developed in consultation with legal experts incorporating Hohfeld’s theory of legal positions [11] but without some of its shortcomings to address contract specific elements. In addition, it has been tested with dozens of contracts to confirm with legal experts its ability to capture legal discourse and its implementation is available to the public. Contract: a collection of obligations and powers between two or more roles, which are assigned to parties during contract execution, and are concerned with two or more assets respectively associated with each role. Contracts may involve subcontracting that assigns to third parties the responsibility for the fulfilment of obligations.

Figure 1 Contract ontology adopted from Symboleo [9] using standard UML notation for class diagrams. An ontology consists of a collection of concepts and relationships for conceptualizing a domain, in our case contracts. The ontology we adopt has been proposed in [9] for the Symboleo contract specification language and is shown in Figure 1. The ontology is based on the Core Legal Ontology UFO-L [10] and was developed in consultation with legal experts incorporating Hohfeld’s theory of legal positions [11] but without some of its shortcomings to address contract specific elements. In addition, it has been tested with dozens of contracts to confirm with legal experts its ability to capture legal discourse and its implementation is available to the public. Contract: a collection of obligations and powers between two or more roles, which are assigned to parties during contract execution, and are concerned with two or more assets respectively associated with each role. Contracts may involve subcontracting that assigns to third parties the responsibility for the fulfilment of obligations.

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Table 1. Comparison of manual and assisted annotation of Rental agreement and Freight agree- ment contracts. Table 2. Average Recall and Precision for concept annotation of both contracts.
Table 1. Comparison of manual and assisted annotation of Rental agreement and Freight agree- ment contracts. Table 2. Average Recall and Precision for concept annotation of both contracts.
The results of the experiment did not identify a reduction in annotation time when supported by ContrattoA, as the average annotation time was 23:41 min for ContrattoA- assisted annotation compared to 22:16 min for manual annotation. ContrattoA support was found to be more effective for annotation quality for both contracts and most of the concepts. Except for precision in the annotation of power, the support of ContrattoA was found to improve performance in most cases, while being comparable for the rest. Generally, precision results were higher than recall ones for all concepts. Moreover, the results suggest a high degree of variability for different concepts. That is not surprising, as some concepts, such as power, are more difficult to grasp than others, and therefore more difficult to identify in text.
The results of the experiment did not identify a reduction in annotation time when supported by ContrattoA, as the average annotation time was 23:41 min for ContrattoA- assisted annotation compared to 22:16 min for manual annotation. ContrattoA support was found to be more effective for annotation quality for both contracts and most of the concepts. Except for precision in the annotation of power, the support of ContrattoA was found to improve performance in most cases, while being comparable for the rest. Generally, precision results were higher than recall ones for all concepts. Moreover, the results suggest a high degree of variability for different concepts. That is not surprising, as some concepts, such as power, are more difficult to grasp than others, and therefore more difficult to identify in text.
Table 3. Comparison of performance measures for powers and obligations. Despite the effort required for refinement, the results are encouraging as they lend support to the possibility of efficient y annotating a diverse set of contracts using a common grammar for powers and obligations. It remains to be seen whether similar results can be obtained with different sets of c identification of lexical patterns cou ontracts without further refinements of the tool. The d be improved with the use of Machine Learning (ML) techniques and the use of such tools is a topic for future work. For further development and experimentation, the time and effort required to refine the tool could be accounted for in the evaluation of tool support to human annotation.
Table 3. Comparison of performance measures for powers and obligations. Despite the effort required for refinement, the results are encouraging as they lend support to the possibility of efficient y annotating a diverse set of contracts using a common grammar for powers and obligations. It remains to be seen whether similar results can be obtained with different sets of c identification of lexical patterns cou ontracts without further refinements of the tool. The d be improved with the use of Machine Learning (ML) techniques and the use of such tools is a topic for future work. For further development and experimentation, the time and effort required to refine the tool could be accounted for in the evaluation of tool support to human annotation.