As head of data science and domain architect for Legal Technology Solutions, Mirko’s role is to build and lead the data science lab. Before joining Clifford Chance in January 2018, he gained 18 years of experience, working in a wide range of industries including financial services, manufacturing, international security and pharmaceuticals. Mirko always worked in technical roles close to research and delivered advanced applications and systems. He has always been interested in cluster computing, parallel programming, optimisation and all the mathematical aspects involved. Mirko strongly believes that the work that we do in Clifford Chance research department offers a unique opportunity to shape and change the legal sector which is why he also work closely with universities for research.
One of the principal tasks of machine learning with major applications is text classification. Th... more One of the principal tasks of machine learning with major applications is text classification. This paper focuses on the legal domain and, in particular, on the classification of lengthy legal documents. The main challenge that this study addresses is the limitation that current models impose on the length of the input text. In addition, the present paper shows that dividing the text into segments and later combining the resulting embeddings with a BiLSTM architecture to form a single document embedding can improve results. These advancements are achieved by util-ising a simpler structure, rather than an increasingly complex one, which is often the case in NLP research. The dataset used in this paper is obtained from an online public database containing lengthy legal documents with highly domain-specific vocabulary and thus, the comparison of our results to the ones produced by models implemented on the commonly used datasets would be unjustified. This work provides the foundation for future work in document classification in the legal field.
One of the principal tasks of machine learning with major applications is text classification. Th... more One of the principal tasks of machine learning with major applications is text classification. This paper focuses on the legal domain and, in particular, on the classification of lengthy legal documents. The main challenge that this study addresses is the limitation that current models impose on the length of the input text. In addition, the present paper shows that dividing the text into segments and later combining the resulting embeddings with a BiLSTM architecture to form a single document embedding can improve results. These advancements are achieved by util-ising a simpler structure, rather than an increasingly complex one, which is often the case in NLP research. The dataset used in this paper is obtained from an online public database containing lengthy legal documents with highly domain-specific vocabulary and thus, the comparison of our results to the ones produced by models implemented on the commonly used datasets would be unjustified. This work provides the foundation for future work in document classification in the legal field.
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Papers by Mirko Bernardoni