Using a Logic Programming Framework to Control Database Query Dialogues in Natural Language

We present a natural language question/answering system to interface the University of Évora databases that uses clarification dialogs in order to clarify user questions. It was developed in an integrated logic programming framework, based on constraint logic programming using the GnuProlog(-cx) language [2,11] and the ISCO framework [1]. The use of this LP framework allows the integration of Prolog-like inference mechanisms with classes and inheritance, constraint solving algorithms and provides the connection with relational databases, such as PostgreSQL. This system focus on the questions’ pragmatic analysis, to handle ambiguity, and on an efficient dialogue mechanism, which is able to place relevant questions to clarify the user intentions in a straightforward manner. Proper Nouns resolution and the pp-attachment problem are also handled. This paper briefly presents this innovative system focusing on its ability to correctly determine the user intention through its dialogue capability

Stacking classifiers for anti-spam filtering of e-mail

We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or "spam", floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications

Article 9 of the E.C.H.R. in light of newer findings of the case law of the E.C.H.R. during the years 2018-2023

L’art. 9 della Convenzione E.D.U. secondo la giurisprudenza recente della Corte E.D.U. (2018-2023) ABSTRACT: The right to religious freedom is constantly changing, following the contemporary treatment of the religious phenomenon within the public sphere of the states. This finding is also evident in the ECHR's jurisprudence, which since the Kokkinakis case seems to have made great progress in the development of the more specific manifestations of religious freedom, such as religious autonomy. In this context, an attempt is made to draw conclusions regarding the right of religious freedom under the related ECHR’s case law. SOMMARIO: 1. Introduction - 2. Freedom of Assembly and Association - 3. Respect for family and private life - 4. Religious freedom in prison - 5. Religious Minorities: the example of Jehovah's Witnesses - 6. The example of Greece - 7. Conclusion

LEGAL-BERT : The Muppets straight out of law school

BERT has achieved impressive performance in several NLP tasks. However, there has been limited investigation on its adaptation guidelines in specialised domains. Here we focus on the legal domain, where we explore several approaches for applying BERT models to downstream legal tasks, evaluating on multiple datasets. Our findings indicate that the previous guidelines for pre-training and fine-tuning, often blindly followed, do not always generalize well in the legal domain. Thus we propose a systematic investigation of the available strategies when applying BERT in specialised domains. These are: (a) use the original BERT out of the box, (b) adapt BERT by additional pre-training on domain-specific corpora, and (c) pre-train BERT from scratch on domain-specific corpora. We also propose a broader hyper-parameter search space when fine-tuning for downstream tasks and we release LEGAL-BERT, a family of BERT models intended to assist legal NLP research, computational law, and legal technology applications

Neural legal judgment prediction in English

Legal judgment prediction is the task of automatically predicting the outcome of a court case, given a text describing the case's facts. Previous work on using neural models for this task has focused on Chinese; only feature-based models (e.g., using bags of words and topics) have been considered in English. We release a new English legal judgment prediction dataset, containing cases from the European Court of Human Rights. We evaluate a broad variety of neural models on the new dataset, establishing strong baselines that surpass previous feature-based models in three tasks: (1) binary violation classification; (2) multi-label classification; (3) case importance prediction. We also explore if models are biased towards demographic information via data anonymization. As a side-product, we propose a hierarchical version of BERT, which bypasses BERT's length limitation