Lexical-Morphological Modeling for Legal Text Analysis

Carvalho, Danilo; Nguyen, Minh-Tien; Tran, Chien-Xuan; Nguyen, Minh-Le

doi:10.1007/978-3-319-50953-2_21

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…Ha et al show that similarity scoring can be used for entailment detection [9], however, this may only work for low-level inferences. Instead, Carvalho et al suggest to use classifiers with a rich feature set [4]. We incorporate an exact matching component for obvious cases of positive entailment in our solution and train a classifier for the majority of cases.…”

Section: Thresholdingmentioning

confidence: 99%

Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions

Wehnert¹,

Hoque²,

Fenske³

et al. 2019

Preprint

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Getting an overview over the legal domain has become challenging, especially in a broad, international context. Legal question answering systems have the potential to alleviate this task by automatically retrieving relevant legal texts for a specific statement and checking whether the meaning of the statement can be inferred from the found documents. We investigate a combination of the BM25 scoring method of Elasticsearch with word embeddings trained on English translations of the German and Japanese civil law. For this, we define criteria which select a dynamic number of relevant documents according to threshold scores. Exploiting two deep learning classifiers and their respective prediction bias with a threshold-based answer inclusion criterion has shown to be beneficial for the textual entailment task, when compared to the baseline.

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Section: Thresholdingmentioning

confidence: 99%

Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions

Wehnert¹,

Hoque²,

Fenske³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The work of charge prediction is also related to the legal assistant systems. In this field, answering legal questions and searching relevant cases have been extensively studied [13], [42], [43]. Some attempts consider charge prediction as a part of the automatic judgment prediction [44]- [46].…”

Section: Introductionmentioning

confidence: 99%

A Neural-Network-Based Model of Charge Prediction via the Judicial Interpretation of Crimes

Kang

Wang

et al. 2020

IEEE Access

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The neural-network-based charge prediction, which is to predict the defendants' charges from the criminal case documents via neural network, has been a development-potential affair in artificial intelligence (AI) based legal assistant system and made some achievements. Neural network is playing important role to capture deep information in current work. However, charge prediction suffers from serious data imbalance in real-world situation. Only high-frequency charges are easy to be predicted whereas plenty of low-frequency ones are hard to be hold. Furthermore, the presence of confusing charges makes prediction worse. Here, we propose a novel model of charge prediction via the judicial interpretation of crimes (CPJIC) to provide more accurate charge prediction. The concept of crime interpretation is introduced into CPJIC, which alleviates the problems resulted from data imbalance and confusing charges. With the technique of embedding, both fact description and crime interpretation are embedded into a low-dimensional vector space as well as a neural network, delivering implemented computable charge prediction. The experimental results demonstrate that CPJIC can identify the low-frequency and confusing charges better than previous work.

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“…This work showed the best LIR performance in 2016. Our previous main work in COLIEE [4] introduced a ranking method called R 2 N C (Ranking Related N-gram Collections), based on a mixed size n-gram language model, which used links between the documents (articles) in the legal corpus to build n-gram collections for each of them, and a variant of TF-IDF scoring to rank them. It achieved a LIR 2nd place in 2015.…”

Section: Related Workmentioning

confidence: 99%

“…The relevance analysis stage was done entirely with R 2 N C [4], which can be summarized in the following process: 9. Store the model.…”

Section: Relevance Analysismentioning

confidence: 99%

Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities

Carvalho¹,

Tran²,

Tran³

et al.

EPiC Series in Computing

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Legal professionals worldwide are currently trying to get up-to-pace with the explosive growth in legal document availability through digital means. This drives a need for high efficiency Legal Information Retrieval (IR) and Question Answering (QA) methods. The IR task in particular has a set of unique challenges that invite the use of semantic motivated NLP techniques. In this work, a two-stage method for Legal Information Retrieval is proposed, combining lexical statistics and distributional sentence representations in the context of Competition on Legal Information Extraction/Entailment (COLIEE). The combination is done with the use of disambiguation rules, applied over the rankings obtained through n-gram statistics. After the ranking is done, its results are evaluated for ambiguity, and disambiguation is done if a result is decided to be unreliable for a given query. Competition and experimental results indicate small gains in overall retrieval performance using the proposed approach. Additionally, an analysis of error and improvement cases is presented for a better understanding of the contributions.

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Lexical-Morphological Modeling for Legal Text Analysis

Cited by 14 publications

References 12 publications

Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions

Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions

A Neural-Network-Based Model of Charge Prediction via the Judicial Interpretation of Crimes

Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities

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