Minh Nguyên

Natural language processing techniques contribute more and more in analyzing legal documents recently, which supports the implementation of laws and rules using computers. Previous approaches in representing a legal sentence often based on logical patterns that illustrate the relations between concepts in the sentence, often consist of multiple words. Those representations cause the lack of semantic information at the word level. In our work, we aim to tackle such shortcomings by representing legal texts in the form of abstract meaning representation, a graph-based semantic representation that gains lots of polarity in NLP community recently. We present our study in AMR Parsing and AMR-to-text Generation specifically for legal domain. We also introduce JCivilCode, a human-annotated legal AMR dataset which was created and verified by a group of linguistic and legal experts. We conduct an empirical evaluation of various approaches in parsing and generating AMR on our own dataset and show the current challenges. Based on our observation, we propose our domain adaptation method applying in the training phase and decoding phase of a neural AMR-to-text generation model. Our method improves the quality of text generated from AMR graph compared to the baseline model. 2018; and “Legal Text Generation fromMeaning Representation”, published in the 32nd International Conference on Legal Knowledge and Information Systems 2019.).

We present our method for tackling a legal case retrieval task by introducing our method of encoding documents by summarizing them into continuous vector space via our phrase scoring framework utilizing deep neural networks. On the other hand, we explore the benefits from combining lexical features and latent features generated with neural networks. Our experiments show that lexical features and latent features generated with neural networks complement each other to improve the retrieval system performance. Furthermore, our experimental results suggest the importance of case summarization in different aspects: using provided summaries and performing encoded summarization. Our approach achieved F1 of 65.6% and 57.6% on the experimental datasets of legal case retrieval tasks.

This paper investigates the task of reference resolution in the legal domain. This is a new interesting task in Legal Engineering research. The goal is to create a system which can automatically detect references and then extracts their referents. Previous work limits itself to detect and resolve references at the document targets. In this paper, we go a step further in trying to resolve references to sub-document targets. Referents extracted are the smallest fragments of texts in documents, rather than the entire documents that contain the referenced texts. Based on analyzing the characteristics of reference phenomena in legal texts, we propose a four-step framework to deal with the task: mention detection, contextual information extraction, antecedent candidate extraction, and antecedent determination. We also show how machine learning methods can be exploited in each step. The final system achieves 80.06 % in the F1 score for detecting references, 85.61 % accuracy for resolving them, and 67.02 % in the F1 score for the end-to-end setting task on the Japanese National Pension Law corpus.

This article addresses the problem of automatically extracting legal indices which express the important contents of legal documents. Legal indices are not limited to single-word keywords and compound-word keywords, they are also clause keywords. We approach index extraction using structural information of Japanese sentences, i.e. chunks and clauses. Based on the assumption that legal indices are composed of important tokens from the documents, extracting legal indices is treated as a problem of collecting chunks and clauses that contain as many important tokens as possible. Each token is assigned a weight which is a statistical score, e.g. TF–IDF and Okapi BM25, to indicate its importance. The importance of a chunk or clause is determined based on the average weight of tokens included in that chunk or clause. Then, highly weighted chunks and clauses are recognized as the indices for legal documents. The experimental results on Japanese National Pension Act data show that our proposed method achieves better performance than TextRank, the most popular unsupervised method in extracting single-word and compound-word keywords. In addition, this approach is also applicable to extract clause keywords with high performance.

The importance of dignity as an ethical and political concept was raised by Kant. Although contemporaries have harassed the considerable rhetorical force of dignity to good effect, neither the content of the concept nor its precise place in political theory have received much, let alone sufficient, discussion. We owe to philosophers in the Kantian tradition what little understanding we have of dignity. This dissertation, however, takes a different point of departure. More at home with the thought of the ancients, it pursues the intuition that dignity is an ethical virtue. In particular, on the proposed analysis dignity turns out to be a second-order unifying virtue. To be dignified is to possess the effective motivation to act out of a concern for the goodness of one's character. Since character is determined by the possession of the virtues and vices, dignity as a virtue is a basic concern to develop, express and maintain the first-order virtues of character in oneself. ;Following Aristotle's view on the relation between virtue and emotion, the analysis of dignity proceeds by developing the function of an emotion thought to be constitutive of dignity, namely contempt. It is concluded that contempt functions to enable the motivation to develop good character by closing off intentions to vicious action. The mechanism by which contempt enables motivation is discussed. ;Determining the precise role the concept of dignity plays in political theory requires establishing something more preliminary, namely, the type of theory dignity is to function in. Continuing with the virtue theme, the conceptual structure of what is called a politics of virtue is investigated. Distinguishing such a theory from the two types of theory exemplified by Martha Nussbaum's and John Rawls' approaches, the general structure of a politics of virtue is proposed. Lastly, a suggestion is made on how dignity as virtue would fit into, and be used to develop, the proposed schema.