3,479 research outputs found
Beauty is Truth and Truth Beauty : How Intuitive Insights Shape Legal Reasoning and the Rule of Law
Scientists have long recognized two distinct forms of human thought. “Type 1” reasoning is unconscious, intuitive, and specializes in finding complex patterns. It is typically associated with the aesthetic emotion that John Keats called “beauty.” “Type 2” reasoning is conscious, articulable, and deductive. Scholars usually assume that legal reasoning is entirely Type 2. However, critics from Holmes to Posner have protested that unconscious and intuitive judgments are at least comparably important. This Article takes the conjecture seriously by asking what science can add to our understanding of how lawyers and judges interpret legal texts. The analysis is overdue. Humanities scholars have long invoked findings from cognitive psychology, brain imaging, and neural network theory to argue that postmodern interpretations that ignore texts in favor of politics and cultural explanations are hopelessly incomplete. Similar arguments should be a fortiori stronger in law, where judges and scholars routinely stress the detailed wording of texts. The Article begins by reviewing previous attempts to apply literary theory to legal texts. We argue that the main failing of this literature is that it says little or nothing about how judges and advocates choose between competing legal interpretations. Section II argues that the best way to fill this gap is to ask what scientists have learned about the brain. This includes the fundamental insight that most human thought processes rely on both Type 1 and Type 2 methods. The Article also documents the surprising cognitive psychology result that Type 1 judgments show significant universality, i.e. that humans who study subjects for long periods often make similar choices without regard to the societies they were born into. Section III extends these arguments to law by arguing that legal judgment frequently turns on the brain’s Type 1 pattern recognition machinery. The next two Sections build on this foundation to construct an explicit theory of how Type 1 thinking enters into legal reasoning and outcomes. Section IV begins by reviewing nineteenth century theories that claimed a leading role for intuitive reasoning in public policy. Section V updates these theories to accommodate the relatively weak statistical correlations that psychologists have documented, arguing that modern court systems amplify these signals in approximately determinate ways. It also explains why court systems that emphasize close textual analysis are able to resist erosion from competing incentives like cronyism and judicial activism. Section VI builds on these theory insights to suggest specific policy prescriptions
Attentive Deep Neural Networks for Legal Document Retrieval
Legal text retrieval serves as a key component in a wide range of legal text
processing tasks such as legal question answering, legal case entailment, and
statute law retrieval. The performance of legal text retrieval depends, to a
large extent, on the representation of text, both query and legal documents.
Based on good representations, a legal text retrieval model can effectively
match the query to its relevant documents. Because legal documents often
contain long articles and only some parts are relevant to queries, it is quite
a challenge for existing models to represent such documents. In this paper, we
study the use of attentive neural network-based text representation for statute
law document retrieval. We propose a general approach using deep neural
networks with attention mechanisms. Based on it, we develop two hierarchical
architectures with sparse attention to represent long sentences and articles,
and we name them Attentive CNN and Paraformer. The methods are evaluated on
datasets of different sizes and characteristics in English, Japanese, and
Vietnamese. Experimental results show that: i) Attentive neural methods
substantially outperform non-neural methods in terms of retrieval performance
across datasets and languages; ii) Pretrained transformer-based models achieve
better accuracy on small datasets at the cost of high computational complexity
while lighter weight Attentive CNN achieves better accuracy on large datasets;
and iii) Our proposed Paraformer outperforms state-of-the-art methods on COLIEE
dataset, achieving the highest recall and F2 scores in the top-N retrieval
task.Comment: Preprint version. The official version will be published in
Artificial Intelligence and Law journa
A deep learning framework for contingent liabilities risk management : predicting Brazilian labor court decisions
Estimar o resultado de um processo em litĂgio Ă© crucial para muitas organizações. Uma aplicação especĂfica sĂŁo os "Passivos Contingenciais", que se referem a passivos que podem ou nĂŁo ocorrer dependendo do resultado de um processo judicial em litĂgio. A metodologia tradicional para estimar essa probabilidade baseia-se na opiniĂŁo de um advogado quem determina a possibilidade de um processo judicial ser perdido a partir de uma avaliação quantitativa. Esta tese apresenta a um modelo matemático baseado numa arquitetura de Deep Learning cujo objetivo Ă© estimar a probabilidade de ganho ou perda de um processo de litĂgio, principalmente para ser utilizada na estimação de Passivos Contingenciais. A arquitetura, diferentemente do mĂ©todo tradicional, oferece um maior grau de confiança ao prever o resultado de um processo legal em termos de probabilidade e com um tempo de processamento de segundos. AlĂ©m do resultado primário, a arquitetura estima uma amostra dos casos mais semelhantes ao processo estimado, que servem de apoio para a realização de estratĂ©gias de litĂgio. Nossa arquitetura foi testada em duas bases de dados de processos legais: (1) o Tribunal Europeu de Direitos Humanos (ECHR) e (2) o 4Âş Tribunal Regional do Trabalho brasileiro (4TRT). Ela estimou de acordo com nosso conhecimento, o melhor desempenho já publicado (precisĂŁo = 0,906) na base de dados da ECHR, uma coleção amplamente utilizada de processos legais, e Ă© o primeiro trabalho a aplicar essa metodologia em um tribunal de trabalho brasileiro. Os resultados mostram que a arquitetura Ă© uma alternativa adequada a ser utilizada contra o mĂ©todo tradicional de estimação do desfecho de um processo em litĂgio realizado por advogados. Finalmente, validamos nossos resultados com especialistas que confirmaram as possibilidades promissoras da arquitetura. Assim, nos incentivamos os acadĂ©micos a continuar desenvolvendo pesquisas sobre modelagem matemática na área jurĂdica, pois Ă© um tema emergente com um futuro promissor e aos usuários a utilizar ferramentas baseadas como a desenvolvida em nosso trabalho, pois fornecem vantagens substanciais em termos de precisĂŁo e velocidade sobre os mĂ©todos convencionais.Estimating the likely outcome of a litigation process is crucial for many organizations. A specific application is the “Contingents Liabilities,” which refers to liabilities that may or may not occur depending on the result of a pending litigation process (lawsuit). The traditional methodology for estimating this likelihood is based on the opinion from the lawyer’s experience which is based on a qualitative appreciation. This dissertation presents a mathematical modeling framework based on a Deep Learning architecture that estimates the probability outcome of a litigation process (accepted & not accepted) with a particular use on Contingent Liabilities. The framework offers a degree of confidence by describing how likely an event will occur in terms of probability and provides results in seconds. Besides the primary outcome, it offers a sample of the most similar cases to the estimated lawsuit that serve as support to perform litigation strategies. We tested our framework in two litigation process databases from: (1) the European Court of Human Rights (ECHR) and (2) the Brazilian 4th regional labor court. Our framework achieved to our knowledge the best-published performance (precision = 0.906) on the ECHR database, a widely used collection of litigation processes, and it is the first to be applied in a Brazilian labor court. Results show that the framework is a suitable alternative to be used against the traditional method of estimating the verdict outcome from a pending litigation performed by lawyers. Finally, we validated our results with experts who confirmed the promising possibilities of the framework. We encourage academics to continue developing research on mathematical modeling in the legal area as it is an emerging topic with a promising future and practitioners to use tools based as the proposed, as they provides substantial advantages in terms of accuracy and speed over conventional methods
Selecting and Generating Computational Meaning Representations for Short Texts
Language conveys meaning, so natural language processing (NLP) requires representations of meaning. This work addresses two broad questions: (1) What meaning representation should we use? and (2) How can we transform text to our chosen meaning representation? In the first part, we explore different meaning representations (MRs) of short texts, ranging from surface forms to deep-learning-based models. We show the advantages and disadvantages of a variety of MRs for summarization, paraphrase detection, and clustering. In the second part, we use SQL as a running example for an in-depth look at how we can parse text into our chosen MR. We examine the text-to-SQL problem from three perspectives—methodology, systems, and applications—and show how each contributes to a fuller understanding of the task.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143967/1/cfdollak_1.pd
Artificial intelligence and the limits of the humanities
The complexity of cultures in the modern world is now beyond human
comprehension. Cognitive sciences cast doubts on the traditional explanations
based on mental models. The core subjects in humanities may lose their
importance. Humanities have to adapt to the digital age. New, interdisciplinary
branches of humanities emerge. Instant access to information will be replaced
by instant access to knowledge. Understanding the cognitive limitations of
humans and the opportunities opened by the development of artificial
intelligence and interdisciplinary research necessary to address global
challenges is the key to the revitalization of humanities. Artificial
intelligence will radically change humanities, from art to political sciences
and philosophy, making these disciplines attractive to students and enabling
them to go beyond current limitations.Comment: 39 pages, 1 figur
08091 Abstracts Collection -- Logic and Probability for Scene Interpretation
From 25.2.2008 to Friday 29.2.2008, the Dagstuhl Seminar 08091 ``Logic and Probability for Scene Interpretation\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper
Development of a recommendation system for scientific literature based on deep learning
Dissertação de mestrado em BioinformaticsThe previous few decades have seen an enormous volume of articles from the scientific commu nity on the most diverse biomedical topics, making it extremely challenging for researchers to
find relevant information. Methods like Machine Learning (ML) and Deep Learning (DL) have
been used to create tools that can speed up this process. In that context, this work focuses
on examining the performance of different ML and DL techniques when classifying biomedical
documents, mainly regarding their relevance to given topics. To evaluate the different techniques,
the dataset from the BioCreative VI Track 4 challenge was used. The objective of the challenge
was to identify documents related to protein-protein interactions altered by mutations, a topic
extremely important in precision medicine. Protein-protein interactions play a crucial role in the
cellular mechanisms of all living organisms, and mutations in these interaction sites could be
indicative of diseases.
To handle the data to be used in training, some text processing methods were implemented
in the Omnia package from OmniumAI, the host company of this work. Several preprocessing
and feature extraction methods were implemented, such as removing stopwords and TF-IDF,
which may be used in other case studies. They can be used either with generic text or biomedical
text. These methods, in conjunction with ML pipelines already developed by the Omnia team,
allowed the training of several traditional ML models.
We were able to achieve a small improvement on performance, compared to the challenge
baseline, when applying these traditional ML models on the same dataset. Regarding DL, testing
with a CNN model, it was clear that the BioWordVec pre-trained embedding achieved the best
performance of all pre-trained embeddings. Additionally, we explored the application of more
complex DL models. These models achieved a better performance than the best challenge
submission. BioLinkBERT managed an improvement of 0.4 percent points on precision, 4.9
percent points on recall, and 2.2 percent points on F1.As dĂ©cadas anteriores assistiram a um enorme aumento no volume de artigos da comunidade cientĂfica sobre os mais diversos tĂłpicos biomĂ©dicos, tornando extremamente difĂcil para os investigadores encontrar informação relevante. MĂ©todos como Aprendizagem Máquina (AM) e Aprendizagem Profunda (AP) tem sido utilizados para criar ferramentas que podem acelerar este processo. Neste contexto, este trabalho centra-se na avaliação do desempenho de diferentes tĂ©cnicas de AM e AP na classificação de documentos biomĂ©dicos, principalmente no que diz respeito Ă sua relevância para determinados tĂłpicos. Para avaliar as diferentes tĂ©cnicas, foi utilizado o conjunto de dados do desafio BioCreative VI Track 4. O objectivo do desafio era identificar documentos relacionados com as interações proteĂna-proteĂna alteradas por mutações, um tĂłpico extremamente importante na medicina de precisĂŁo. As interacções proteĂna-proteĂna desempenham um papel crucial nos mecanismos celulares de todos os organismos vivos, e as mutações nestes locais de interacção podem ser indicativas de doenças. Para tratar os dados a utilizar no treino, alguns mĂ©todos de processamento de texto foram implementados no pacote Omnia da OmniumAI, a empresa anfitriĂŁ deste trabalho. Foram implementados vários mĂ©todos de prĂ©-processamento e extracção de caracterĂsticas, tais como a remoção de palavras irrelevantes e TF-IDF, que podem ser utilizados em outros casos de estudos, tanto com texto genĂ©rico quer com texto biomĂ©dico. Estes mĂ©todos, em conjunto com as pipelines de AM já desenvolvidas pela equipa da Omnia, permitiram o treino de vários modelos tradicionais de AM. Conseguimos alcançar uma pequena melhoria no desempenho, em comparação com a linha de referĂŞncia do desafio, ao aplicar estes modelos tradicionais de AM no mesmo conjunto de dados. Relativamente a AP, testando com um modelo CNN, ficou claro que o embedding prĂ©-treinado BioWordVec alcançou o melhor desempenho de todos os embeddings prĂ©-treinados. Adicionalmente, exploramos a aplicação de modelos de AP mais complexos. Estes modelos alcançaram um melhor desempenho do que a melhor submissĂŁo do desafio. BioLinkBERT conseguiu uma melhoria de 0,4 pontos percentuais na precisĂŁo, 4,9 pontos percentuais no recall, e 2,2 pontos percentuais em F1
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