Evaluating Document Representations for Content-Based Legal Literature Recommendations
dc.contributor.author
Ostendorff, Malte
dc.contributor.author
Ash, Elliott
dc.contributor.author
Ruas, Terry
dc.contributor.author
Gipp, Bela
dc.contributor.author
Moreno-Schneider, Julian
dc.contributor.author
Rehm, Georg
dc.date.accessioned
2021-08-31T11:35:20Z
dc.date.available
2021-08-31T11:35:20Z
dc.date.issued
2021-06
dc.identifier.isbn
978-1-4503-8526-8
en_US
dc.identifier.other
10.1145/3462757.3466073
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/503253
dc.description.abstract
Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflect the latest advances in recommender systems and representation learning research. Simultaneously, legal recommender systems are typically evaluated in small-scale user study without any public available benchmark datasets. Thus, these studies have limited reproducibility. To address the gap between research and practice, we explore a set of state-of-the-art document representation methods for the task of retrieving semantically related US case law. We evaluate text-based (e.g., fast-Text, Transformers), citation-based (e.g., DeepWalk, Poincaré), and hybrid methods. We compare in total 27 methods using two silver standards with annotations for 2,964 documents. The silver standards are newly created from Open Case Book and Wikisource and can be reused under an open license facilitating reproducibility. Our experiments show that document representations from averaged fastText word vectors (trained on legal corpora) yield the best results, closely followed by Poincaré citation embeddings. Combining fastText and Poincaré in a hybrid manner further improves the overall result. Besides the overall performance, we analyze the methods depending on document length, citation count, and the coverage of their recommendations.
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computing Machinery
en_US
dc.subject
Legal literature
en_US
dc.subject
Document embeddings
en_US
dc.subject
Document similarity
en_US
dc.subject
Recommender systems
en_US
dc.subject
Transformers
en_US
dc.subject
WikiSource
en_US
dc.subject
Open Case Book
en_US
dc.title
Evaluating Document Representations for Content-Based Legal Literature Recommendations
en_US
dc.type
Conference Paper
dc.date.published
2021-06-21
ethz.book.title
Proceedings of the 18th International Conference on Artificial Intelligence and Law (ICAIL '21)
en_US
ethz.pages.start
109
en_US
ethz.pages.end
118
en_US
ethz.event
18th International Conference on Artificial Intelligence and Law (ICAIL 2021)
en_US
ethz.event.location
Online
en_US
ethz.event.date
June 21-25, 2021
en_US
ethz.notes
Conference lecture held on June 24, 2021.
en_US
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02045 - Dep. Geistes-, Sozial- u. Staatswiss. / Dep. of Humanities, Social and Pol.Sc.::09627 - Ash, Elliott / Ash, Elliott
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02045 - Dep. Geistes-, Sozial- u. Staatswiss. / Dep. of Humanities, Social and Pol.Sc.::09627 - Ash, Elliott / Ash, Elliott
en_US
ethz.date.deposited
2021-05-18T14:03:40Z
ethz.source
SCOPUS
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-08-31T11:35:28Z
ethz.rosetta.lastUpdated
2024-02-02T14:35:32Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/501440
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/484693
ethz.COinS
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