69,186 research outputs found
DoSSIER@ COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval
In this paper, we present our approaches for the case law retrieval and the legal case entailment task in the Competition on Legal Information Extraction/Entailment (COLIEE) 2021. As first stage retrieval methods combined with neural re-ranking methods us- ing contextualized language models like BERT achieved great performance improvements for information retrieval in the web and news domain, we evaluate these methods for the legal domain. A distinct characteristic of legal case retrieval is that the query case and case description in the corpus tend to be long documents and therefore exceed the input length of BERT. We address this challenge by combining lexical and dense retrieval methods on the paragraph-level of the cases for the first stage retrieval. Here we demonstrate that the retrieval on the paragraph-level outperforms the retrieval on the document-level. Furthermore the experiments suggest that dense retrieval methods outperform lexical retrieval. For re-ranking we address the problem of long documents by sum- marizing the cases and fine-tuning a BERT-based re-ranker with the summaries. Overall, our best results were obtained with a com- bination of BM25 and dense passage retrieval using domain-specific embeddings.DoSSIER@ COLIEE 2021Horizon 2020(H2020)Algorithms and the Foundations of Software technolog
Ontology Mapping for a Legal Question Answering System
Legal information retrieval systems, such question answering, use legal ontologies to represent semantic objects, to associate them with legal documents and to make inferences about them. The ontology mapping process can help users to reuse and compare information from different ontologies. In this paper we present a review on legal ontologies and present an approach to ontology mapping based on argumentation. Individual mappings are computed by specialized agents using different mapping approaches. Next, these agents use argumentation to exchange their local results, in order to agree on the obtained mappings. To each argument is associated a strength, representing how confident an agent is in the similarity of two ontology terms. Based on their preferences and confidence of the arguments,
the agents compute their preferred mapping sets. The arguments in such preferred sets are viewed as the set of globally acceptable arguments. This work is part of a question answering system for the legal domain
Multimodal Legal Information Retrieval
The goal of this thesis is to present a multifaceted way of inducing semantic representation from legal documents as well as accessing information in a precise and timely
manner. The thesis explored approaches for semantic information retrieval (IR) in the
Legal context with a technique that maps specific parts of a text to the relevant concept. This technique relies on text segments, using the Latent Dirichlet Allocation (LDA),
a topic modeling algorithm for performing text segmentation, expanding the concept
using some Natural Language Processing techniques, and then associating the text segments to the concepts using a semi-supervised text similarity technique. This solves
two problems, i.e., that of user specificity in formulating query, and information overload, for querying a large document collection with a set of concepts is more fine-grained
since specific information, rather than full documents is retrieved. The second part of the
thesis describes our Neural Network Relevance Model for E-Discovery Information Retrieval. Our algorithm is essentially a feature-rich Ensemble system with different component Neural Networks extracting different relevance signal. This model has been trained
and evaluated on the TREC Legal track 2010 data. The performance of our models across
board proves that it capture the semantics and relatedness between query and document
which is important to the Legal Information Retrieval domain
Automated legal sensemaking: the centrality of relevance and intentionality
Introduction: In a perfect world, discovery would ideally be conducted by the senior litigator who is
responsible for developing and fully understanding all nuances of their client’s legal strategy. Of
course today we must deal with the explosion of electronically stored information (ESI) that
never is less than tens-of-thousands of documents in small cases and now increasingly involves
multi-million-document populations for internal corporate investigations and litigations.
Therefore scalable processes and technologies are required as a substitute for the authority’s
judgment. The approaches taken have typically either substituted large teams of surrogate
human reviewers using vastly simplified issue coding reference materials or employed
increasingly sophisticated computational resources with little focus on quality metrics to insure
retrieval consistent with the legal goal. What is required is a system (people, process, and
technology) that replicates and automates the senior litigator’s human judgment.
In this paper we utilize 15 years of sensemaking research to establish the minimum acceptable
basis for conducting a document review that meets the needs of a legal proceeding. There is
no substitute for a rigorous characterization of the explicit and tacit goals of the senior litigator.
Once a process has been established for capturing the authority’s relevance criteria, we argue
that literal translation of requirements into technical specifications does not properly account for
the activities or states-of-affairs of interest. Having only a data warehouse of written records, it
is also necessary to discover the intentions of actors involved in textual communications. We
present quantitative results for a process and technology approach that automates effective
legal sensemaking
Matching Law Ontologies using an Extended Argumentation Framework based on Confidence Degrees
Law information retrieval systems use law ontologies to represent semantic objects, to associate them with law documents and to make inferences about them. A number of law ontologies have been proposed in the literature, what shows the variety of approaches pointing to the need of matching systems. We present a proposal based on argumentation to match law ontologies, as an approach to be considered for this problem. Argumentation is used to combine different techniques for ontology matching. Such approaches are encapsulated by agents that apply individual matching algorithms and cooperate in order to exchange their local results (arguments). Next, based on their preferences and confidence, the agents compute their preferred matching sets. The arguments in such preferred sets are viewed as
the set of globally acceptable arguments. We show the applicability of our model matching two legal core ontologies: LKIF and CLO
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