A large number of cross-references to various bodies of text are used in legal texts, each serving a different purpose. It is often necessary for authorities and companies to look into certain types of these citations. Yet, there is a lack of automatic tools to aid in this process. Recently, citation graphs have been used to improve the intelligibility of complex rule frameworks. We propose an algorithm that builds the citation graph from a document and automatically labels each edge according to its purpose. Our method uses the citing text only and thus works only on citations whose purpose can be uniquely identified by their surrounding text. This framework is then applied to the US code. This paper includes defining and evaluating a standard gold set of labels that cover a vast majority of citation types which appear in the “US Code“ but are still short enough for practical use. We also proposed a novel Linear-Chain Conditional Random Field (CRF) model that extracts the features required for labeling the citations from the surrounding text. We then analyzed the effectiveness of different clustering methods such as K-means and Support Vector Machine to automatically label each citation with the corresponding label. Besides this, we talk about the practical difficulties of this task and give a comparison of human accuracy compared to our end-to-end algorithm.
Authors:
Ali Sadeghian, Laksshman Sundaram, Daisy Zhe Wang, William F. Hamilton, Karl Branting, Craig Pfeifer
Bibtex:
@Article{Sadeghian2018,
author="Sadeghian, Ali
and Sundaram, Laksshman
and Wang, Daisy Zhe
and Hamilton, William F.
and Branting, Karl
and Pfeifer, Craig",
title="Automatic semantic edge labeling over legal citation graphs",
journal="Artificial Intelligence and Law",
year="2018",
month="Jun",
day="01",
volume="26",
number="2",
pages="127--144",
abstract="A large number of cross-references to various bodies of text are used in legal texts, each serving a different purpose. It is often necessary for authorities and companies to look into certain types of these citations. Yet, there is a lack of automatic tools to aid in this process. Recently, citation graphs have been used to improve the intelligibility of complex rule frameworks. We propose an algorithm that builds the citation graph from a document and automatically labels each edge according to its purpose. Our method uses the citing text only and thus works only on citations who's purpose can be uniquely identified by their surrounding text. This framework is then applied to the US code. This paper includes defining and evaluating a standard gold set of labels that cover a vast majority of citation types which appear in the ``US Code'' but are still short enough for practical use. We also proposed a novel linear-chain conditional random field model that extracts the features required for labeling the citations from the surrounding text. We then analyzed the effectiveness of different clustering methods such as K-means and support vector machine to automatically label each citation with the corresponding label. Besides this, we talk about the practical difficulties of this task and give a comparison of human accuracy compared to our end-to-end algorithm.",
issn="1572-8382",
doi="10.1007/s10506-018-9217-1",
url="https://doi.org/10.1007/s10506-018-9217-1"
}
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